{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "介绍如何在pytorch环境下，使用DeepFool算法攻击基于ImageNet数据集预训练的alexnet模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebook中使用Anaconda中的环境需要单独配置，默认情况下使用的是系统默认的Python环境，以使用advbox环境为例。\n",
    "首先在默认系统环境下执行以下命令，安装ipykernel。\n",
    "\n",
    "    conda install ipykernel\n",
    "    conda install -n advbox ipykernel\n",
    "\n",
    "在advbox环境下激活，这样启动后就可以在界面上看到advbox了。\n",
    "\n",
    "    python -m ipykernel install --user --name advbox --display-name advbox \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#打开调试日志\n",
    "import logging\n",
    "logging.basicConfig(level=logging.INFO,format=\"%(filename)s[line:%(lineno)d] %(levelname)s %(message)s\")\n",
    "logger=logging.getLogger(__name__)\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "import torch.utils.data.dataloader as Data\n",
    "import torch.nn as nn\n",
    "from torchvision import models\n",
    "from adversarialbox.adversary import Adversary\n",
    "from adversarialbox.attacks.deepfool import DeepFoolAttack\n",
    "from adversarialbox.models.pytorch import PytorchModel\n",
    "import numpy as np\n",
    "import cv2\n",
    "from tools import show_images_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#定义被攻击的图片\n",
    "image_path=\"tutorials/cropped_panda.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-688f3b2a4d4b>[line:2] INFO CUDA Available: False\n",
      "pytorch.py[line:66] INFO Finish PytorchModel init\n",
      "base.py[line:87] INFO adversary:\n",
      "         original_label: 388\n",
      "         target_label: 538\n",
      "         is_targeted_attack: True\n",
      "deepfool.py[line:54] INFO min_=-3, max_=3\n",
      "deepfool.py[line:73] INFO select label:538\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n",
      "(1, 3, 224, 224)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "deepfool.py[line:121] INFO iteration=0, f[pre_label]=6.33254623413, f[target_label]=-1.91577374935, f[adv_label]=8.76095581055, pre_label=388, adv_label=266\n",
      "deepfool.py[line:121] INFO iteration=1, f[pre_label]=2.89678573608, f[target_label]=-0.87459141016, f[adv_label]=9.90505599976, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=2, f[pre_label]=0.944459617138, f[target_label]=-0.136466905475, f[adv_label]=10.4868774414, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=3, f[pre_label]=0.350003749132, f[target_label]=0.192997470498, f[adv_label]=10.7110366821, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=4, f[pre_label]=0.254496186972, f[target_label]=0.256794452667, f[adv_label]=10.7316360474, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=5, f[pre_label]=0.253140777349, f[target_label]=0.257781863213, f[adv_label]=10.7320013046, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=6, f[pre_label]=0.250402539968, f[target_label]=0.259774804115, f[adv_label]=10.7327489853, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=7, f[pre_label]=0.244893506169, f[target_label]=0.263800740242, f[adv_label]=10.7343158722, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=8, f[pre_label]=0.233982816339, f[target_label]=0.271917700768, f[adv_label]=10.7374458313, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=9, f[pre_label]=0.212208643556, f[target_label]=0.287769854069, f[adv_label]=10.7436599731, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=10, f[pre_label]=0.168237045407, f[target_label]=0.318640947342, f[adv_label]=10.7548179626, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=11, f[pre_label]=0.0841835960746, f[target_label]=0.377846956253, f[adv_label]=10.7807531357, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=12, f[pre_label]=-0.0758390203118, f[target_label]=0.491921842098, f[adv_label]=10.8438863754, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=13, f[pre_label]=-0.346920758486, f[target_label]=0.690455198288, f[adv_label]=10.9393196106, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=14, f[pre_label]=-0.805594980717, f[target_label]=1.01700508595, f[adv_label]=11.1782722473, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=15, f[pre_label]=-1.23940086365, f[target_label]=1.63333177567, f[adv_label]=11.3831291199, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=16, f[pre_label]=-2.20888876915, f[target_label]=1.77146542072, f[adv_label]=11.7314062119, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=17, f[pre_label]=-2.39025235176, f[target_label]=2.6299393177, f[adv_label]=11.830165863, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=18, f[pre_label]=-3.24088168144, f[target_label]=3.59277629852, f[adv_label]=11.7823562622, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=19, f[pre_label]=-4.4698472023, f[target_label]=4.58672523499, f[adv_label]=11.9470996857, pre_label=388, adv_label=153\n",
      "deepfool.py[line:121] INFO iteration=20, f[pre_label]=-5.09104013443, f[target_label]=6.17052936554, f[adv_label]=10.5380029678, pre_label=388, adv_label=196\n",
      "deepfool.py[line:121] INFO iteration=21, f[pre_label]=-6.29823541641, f[target_label]=8.05016040802, f[adv_label]=12.0893831253, pre_label=388, adv_label=196\n",
      "deepfool.py[line:121] INFO iteration=22, f[pre_label]=-6.4235086441, f[target_label]=11.0038414001, f[adv_label]=12.7778902054, pre_label=388, adv_label=643\n",
      "deepfool.py[line:121] INFO iteration=23, f[pre_label]=-8.59449386597, f[target_label]=13.3700847626, f[adv_label]=14.2170543671, pre_label=388, adv_label=643\n",
      "deepfool.py[line:121] INFO iteration=24, f[pre_label]=-7.90409946442, f[target_label]=16.1156845093, f[adv_label]=16.6487293243, pre_label=388, adv_label=528\n",
      "deepfool.py[line:121] INFO iteration=25, f[pre_label]=-10.9434547424, f[target_label]=19.8184585571, f[adv_label]=19.8184585571, pre_label=388, adv_label=538\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attack success, adversarial_label=538\n",
      "deepfool attack done\n"
     ]
    }
   ],
   "source": [
    "# Define what device we are using\n",
    "logging.info(\"CUDA Available: {}\".format(torch.cuda.is_available()))\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "#cv2默认读取格式为bgr bgr -> rgb   \n",
    "orig = cv2.imread(image_path)[..., ::-1]\n",
    "#转换成224*224\n",
    "orig = cv2.resize(orig, (224, 224))\n",
    "adv=None\n",
    "img = orig.copy().astype(np.float32)\n",
    "\n",
    "#图像数据标准化\n",
    "mean = [0.485, 0.456, 0.406]\n",
    "std = [0.229, 0.224, 0.225]\n",
    "img /= 255.0\n",
    "img = (img - mean) / std\n",
    "\n",
    "#pytorch中图像格式为CHW  \n",
    "#[224,224,3]->[3,224,224]\n",
    "img = img.transpose(2, 0, 1)\n",
    "\n",
    "img = Variable(torch.from_numpy(img).to(device).float().unsqueeze(0)).cpu().numpy()\n",
    "\n",
    "\n",
    "# Initialize the network\n",
    "#Alexnet\n",
    "model = models.alexnet(pretrained=True).to(device).eval()\n",
    "\n",
    "#print(model)\n",
    "\n",
    "#设置为不保存梯度值 自然也无法修改\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# advbox demo\n",
    "m = PytorchModel(\n",
    "    model, None,(-3, 3),\n",
    "    channel_axis=1)\n",
    "\n",
    "#实例化DeepFool \n",
    "attack = DeepFoolAttack(m)\n",
    "attack_config = {\"iterations\": 100, \"overshoot\": 0.02}\n",
    "\n",
    "inputs=img\n",
    "labels = None\n",
    "\n",
    "print(inputs.shape)\n",
    "\n",
    "adversary = Adversary(inputs, labels)\n",
    "\n",
    "#定向攻击\n",
    "tlabel = 538\n",
    "adversary.set_target(is_targeted_attack=True, target_label=tlabel)\n",
    "\n",
    "\n",
    "adversary = attack(adversary, **attack_config)\n",
    "\n",
    "if adversary.is_successful():\n",
    "    print(\n",
    "        'attack success, adversarial_label=%d'\n",
    "        % (adversary.adversarial_label))\n",
    "\n",
    "    adv=adversary.adversarial_example[0]\n",
    "\n",
    "else:\n",
    "    print('attack failed')\n",
    "\n",
    "\n",
    "print(\"deepfool attack done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#格式转换\n",
    "adv = adv.transpose(1, 2, 0)\n",
    "adv = (adv * std) + mean\n",
    "adv = adv * 256.0\n",
    "adv = np.clip(adv, 0, 255).astype(np.uint8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l0=123793 l2=64327.3090996\n"
     ]
    },
    {
     "data": {
      "image/png": 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kOMaIKJ2IiqM/+Gxu3sSGwNrqGpPJhHJUsXTiGBeuXOGD7/8Qq20/cS6Q6QyFIFoRVaCuJ9y8cpmLr53h6qXz3Lq1galqgniiKExjePnypyk3b5LHwLFuxvLCu1E2YL3BV5Ysg+ADUSlCFpBgqeoGHyzWGmKaPRFCg48OH8AGKABvHVkGo7Ihao04TbffJTSWprQUvT7eBdaPr2INBKeJK4usrKwhyrKzuYNWsDpp6B87wenTp+hmHcykoeh0WcwKQmXJo6U2Ea0FFUA8ZFqjdJegAoFICJDle/1EiSICojNEKZTKkmQus/1EESMo9RZYIdiVEmYPYCrNsBecfgYHWGswTb2vjP1UMpOemTJn4vdqHsz8dyDFvjz78++rYhp2CENNOWUwQ3Z7tzOYTbp3NzPPZ/aedksY7E/PNM9gr9279d7e8sPJ9Q3iSFj3PfXkl8fV9XUWFlfYmpRsjcfobheVd/mR//jH8AKNseRFB0EQJeR5zutnXuXsCy9z+dWzbFw4S1mOiNEhEvAhoEWR6Q6mDkwmJWurqzz11Ffy5/78D/OOdzxGjJYiB60FrRRKgQhJcnIW7wzExAwxerxPBBXawYjoUQjeORprEEllJZIKxBjp9xfZ2tpGJP3jdy6SZwWIoppMAE9ZVQSEoreE6A5Zd4GltROsHH8YLzm1h5s7Y0YTQ209Pgo+RmLwQNiV6kAhStPpdMnyHCJonZEVHbI8R4lGqURWIpHgkoQYQiCEwHe8Z+1Iq3GeevLdcfX4CfqLK2yNSrYnY1SvC1mPH/vLP4ojUntH1umDDyil0HnG2VfP8PrLr7Dxymtcv3Ke8dYmMTjEB5xEtChEdcHDcDKi21vmg1/3fn7oz/ww7/2KLyO4ik4uFLkiRNBFRq4j40mJEHF1jVIQNYQYIBiMsQQX8CqilWexyGhuDSnTPIU8ywgmoKJHckXR6bN1YwsRTdEpqFwk7/TxBMxkBM5S1xaH0F9ephcNVi2x8vBjLK+dxpFROWG7qrlxYwxZxKGxDgiW4D1ITGQkGUppsqxDb6FL8IEsQr64iMo0SjRCln4Fonc4l1TKznv+8BHvJ4MBcZY49g2mu6N3OvYG9v3h913XnVIfEjFLU3vRd67vsDSzxDm4Ld9g72bvVuIsId9GfHut3CvqsDbuteew6gaprDelnxyJadGT73kPC70e12/cQGdZOrTmWz/yEdAK5wM6a1VWACHijWXz5nWuXbnEzY0NRltbmHJCtAacJTQ1rqpoxiNygaVuh2Y85qVnn+Xzn/4U1y9fxJRj6nJEVQ2p6iFlPaIqh1STEXU1pm5KrDM4b3DesUvoUYhREVF4HwkzM0znHE3TYK3Fe0/TVIhAked0Ox163S6dokArhRDp9bosLi7Q63bJc02v32VpaYFcK7yzaCV0i4xuUZBnikxBrqCTZ2RaUERUBGnVfrnOyJRGIWiVpDpRap9LSaWmcYlQRQSRIz3uAPDkk+9lodfj2rVr6FyhCkXeUXzkW76ZALgIqDzN+pWgFGAt49EWW5tXuXX1KqPNLUxT4qMjaA9UeFfizARRgcVul6Ya8fLnPsvLn/kkN14/SzPcxtY7jCdbVGaHcTlkNN6hrkvKcoxzNbapIBgiAedBi0J7UK3EW5cOL5qIgqhwJqmujXFY71tJLNLtdSi6Bd28oKszOlqR4VhZ6bG41KfIMxb1mJUTayycPA2dLqVpyIqcTqHoFh26HY0WQcVIodN5kWkypdFZRpFndPMOnbxAiyYTjdJ50iQQiCH1JyWqJXBBputUciSGjHtgkP62g/G+gXQ6OE9/bhMH9l3cVVJI54PpiLw/dnAYhRxW7oBDaxkcvJimntLUvhbslXWw0kPufZYob693cMj62Gyavfbu1jvT/MEhOb5YHIke99GPfhRjDNbWmKZBRHjyya/kqfc9TVnWiNIUnQ5aa7RWCGDqinpSYusG09RE7yB4grN4a8B7og9pBhk9RaYpMkVTT7hw/ixXr1xkMtqiroZU1ZCy3GEy2WE02mZS7lBWY5qmxJgaYwzOOUKMu6qyiBCiEIjtrDRrSVQIwRNbSaqqKjpFTqdbkGV70lqRZywtL9HrFCwt9FlaWqQoOiglWGuoyjFVOSY6QybQ6+T0OwW9Tk6Ra3QM5ErIM41WikwpiiyjU+RkSrcebyMigiYR0z4yEqBVcb5VSOqj3/1RTNPgXY1rDArFl7/rK3nqq9/HpLFElVMUXbJM0Sk00XpMXTHa3KHcLqnLClc2SAjJaMQ2BBOILiLBIzHQyTXL/S6uKrnw2lmunL9MXY6YjLep6iGTcpvJaIvJeIdJucW4HDIuJ1hfU1cl1hqcD4QgoEHptLZjnUfrIq39iECAalxiY0C0Zmdzh16vQ6er0VrIMlBEer0O/YVF8qJgeXGRtbVjlK7PxmbG1nZgeHObppngXU3RySiUZqnXY6HToZMpgjFoFREJxBBQEVSATCStpzau7QeS1j2VSutlCAJtfw3J0OYt0k92MdgvveyTMg4hkTbLIWtHB9MckIcG7P3OEsFs5sHsxUxdByrZrfsO7dste+Zst5x7pJ59DPurHRy4nr04rB2D22Pu3IAvGkeCpB59+GHKyZhup4sNlqWVFb7mgx+gcQ7JFCpLKqpImvl7a7m2scH1jWtU5QTrajKtWlUbaK0pOjmdTk6v10WUYEyD9xalIo2pGI632dq5Rd2MMe1h6xF1PaKqJzSmlaKcaVVinhhjWlZSKi0ui0ZJhmiNyjQ60+g8R2cZSgvTSWdRFAQfqOsaFUELFJlieXGBEANKaRb6fVaWF+kUGVqg28kptODadbFCa/qdnH5RkBEZ72wzGQ2pJmNGw23Gwx0moyGT8ZDRcIvtzZuMd3bSwGka8A4tkUylwU921byRtNh19NekHn3oNFU5plt0CNGxtLTE+z/wQWwUVCdHtCbTGcFFoldYC9c2bnLj8lUmmxW2thRkZFEjQch9Rp51yFVBR3eRIDS2RIkFFajNmNHoJrduXmdSl1hbU9UlvhmzM9piUpdM6gm1K6ltTW0rnKnRQIggeYGngNgnU32iz9CZJlOCBxZOr5NlghmP6R9boN/rIlEYjSZEY1EukoXIsYVlcJFCNOuLyzz08NtZObHIcj/S7yl6vR5lWbXGM9DpFhSdAi1QjneoJjuUkxHD7R3KnTHVqKQcjRmNt9ncvMlkNKSuShpjiR60CLlEtKQbiVGQNC2Dt4LhxMxAn37b05akZgfpPe4Y7P3dDUsXA2aJaTb1gcSDKckM9sreFUtmKz0gcUzTsEdS+5pwAPtJ6iAJ3SHn4MDvvvvZq3N/WXcub1/0TMY71P5F4UiQ1Cc/8e85fnwd01T0en3e8c530uv32RmN6fYXUVlBECHGZFK7vbXFC889z+c/+wyXLp6nqiu8CsnySkUcHhcDJjhq22CcxUskZpD1coqFnJB5bKyxvsS6EmMm1E0iJ+dqgrcIaQhvNfmtFVyGVjmqJSbJdLKQUomgOp0O3W6XopPMvrMsS+oSgaLIWFrq0+lkrUl7RVFkFB1NjA7bNGRK6HULVIx4UyPegmkoJLDUyVnu5uTRc2PjIudefomXn32Ol559jhc//zme++wzPPvMZ3j2s8/w+Wee4dUzL7N54wauqbFNQ1OVycpL0d5f2LPyeqA94P7wyU99nLW1NaxtKHpd3vHud7Gw3Gd7Z4duv4fkCq/TjN9Zz62tTT773Od5/qXnuHjlNRo/wfYithtoug7bhZCD6XgmqsEJKK+IdcPiomZhNSMuR5quJ4QJdbWFt2OsH6L8GEVDJ49oFKYOCAp0hvFJndpD0JIRvUVpiHlAZ4qsW7Cw2GU1TjjeDfR6PawTfIhY41lY6LJ2co1iUWPKCcE16CLDd6D0FdVwh8xWdAtQQQi3bpKXFX57QmwiK/0Oy50MqRpuXbnIuZfP8MrzL3LmhRc488JneeH53+HZ5z7D5575DM8/+xleevFFNrc2CdbiygY3GRMkorMIOIg+GdwQkQe/hH1vDAYzFmqDXRXWYC+aPVI4JPs+yWIwEzY4JMX+sD2SGezWfRuR7a/s0PjbqHGmoQdlm8HMyWGt3ZdoJvHgDtEHn0l6doP9ZDoTt1v3gXxvFo4ESV2+dJHgLN1eD2ssa+vHqa0jADYEAuBDsjBSCNEHCCHtP4meqCJeRbwSgkrWb14gZgqvJBFJrjDBIblmYXmBvJvRW+wSgqOpK8rJmPFomCQz00CMSTrLNEpniE7SU1LcC6IUgm6vFaIVSmu01mR5Rl6k2WxRZHhnUaQF+qau8c4k9QmBGDwxepSCopOltaoYkgVjnqGIBGfpaMVir8vKYp/lfhffVNy8tsHlC69z9dJFLl84z9kzL3PmpRe5fP48Vy9f5OLr5xgPt4nBM9zZYuvWzbRfJ1MEb5JKMrRGIHf9DtrRwKVLF5JUWfSwxrF87AQ7lcEBtfMEBTZ4tFZkaKKzSDQEW5IrS8BjM8FlQsginkDjHUECXgKQofIuLs/IckXeX8TnBarTwQePszV1NWa4vcVoNMY2DgmKbidjYblHpnIAJMsIovEIMQQy0dSloVjqIJmglaBFyDKF769gdZ9er093QbP28DJFrmnqCoVD5yqp6cSDNkju6XZzCpX+HXQLTW+pS77QwxjH+lqXpW6X1cUF1lcXiLbh1tUNrl46z42rVzh/4Ryvvvoir7z8AlcuvM7VSxe4fPEc450tvHVs3bjF5vUbCB4VScZDU7V5SCrDo44Bgz0rvhmy2Lue/T14vne9L+XgdnLYl/pA+bt/D8myO+gfVtC+LIO9VgxmQ+6Vd0qYMxnvisE+grmNbKbkdIdqZwl/mvd+ar1fHAmS6nW7jIcjxts7PPLI21g/cTxZ8HUKUAoXIjZ4nLU4Y/DGEK0lOJes6CRtGYoCUYQgEEQICGiFZBq0QuWavFug8wwfPVVTtYvCEILHmIayLKnrGu89eZ4naUjnSXpqjQxihBAiYbrPRLUrQDEm03Pvcc7hXHIcXNcVw+GQ4XCbsppQ1zVN01BVFc4ZnDE4a/DWQnCoVh2YaSFTUGSgCeANKnj6nZxHTp1kudfDViXXNy5xfeMSN69ucOvaBlcunefihde5duUy450tzr72Ci+98Bx1NcE1FcEZtETqcoyxNaFVax51dIsO4+GIcjzkkUcf4cSpE+Ra6HQKRGf4ILgIwTliMIg1aOfIgeAjUWcEUQSl0kQmE4JkRFUQRREzockFm2f4rEvWyYniCbEhAt5Fog80ztE4S1XVmLKBkNHpdsnzgk7eIc8EnSnqKIgky0mnIioXMpU2RyjRjGOXkdNEEZQ3bJy/yZlnXmPrynW2t0eUZY0zjqquqSqbrAFDAGtQwdHLhSITYtRkWuh1IoX2RFsSy4q+1jx8+iSLvT6YhqtXLrFx5QrXrlzj+pUrbFw8z8UL57l2+TKj7Vu8fv4ML734LI2Z0FQlIVhiCDQmrbkFZ/APzhn2feNQldlgb4AfHIjbl+6w8u5Cb4n/Bnvl30aOs3lmS5iNaFt1W0MGh97DwTT7ihwM7ng/d5Z0BneOGwwOkN3+38GBgn/fktTJ42t81Ve/j3e86wkef+IJur0uw/EIH6E2FpVlFEVBnhcggneJrDSQKdWq5GjJaUpYQky2bzTOYpwjKzKWjy3RX14AFSmbkp2dHSajEmscWmkWFhZYWFjYXUdSSqO0aolI8BFcABcizvm0QZZkEm69wzhLY21rCGKTFVlwab9VKzU5Z6irknIybgmrpqlrrKmTqa+3EB0SHBIdmoAKDhUsHR1ZW+7z7ice56kn381jD51k58Y1Lp97jQuvvsK5V17i3CsvcensGc6/+hLPfuZT/D//7GN89rf/Pa4aUU922LxxletXLnHz+gbXNy5zdeMSt65debCd4D5w6tQaX/U1qZ982eNPsNgtqMYTgoO6NmiV0827dPIeEgVvPNSBWCkgx0eIzuNdQKImxPROnc9xocNYArawqCVNvrZCf2UFiUI5KdkZblNWDXVt6ObJkKcoikRIuSK4SLffRXtHJ3pcjNQkDyFl41h/6BjKeHzQVI3DhciktpjRiMXCEt2ErBvpd4VeR2CpQxkD48oz2RyDsfiJpRnaj0/mAAAgAElEQVR5RsZTOQc4rPHkmRBMnQiqqhBvyPLI2voKTz/9Xp568ss5vb7OjYuXuHbuLJfOvsa5l1/m/Csvc+W1M5x/9WWeeeZT/NLH/imf/uRvEuyEuhxy/doG165e4sbG5ZbgLnJr4/ID7gX3xp0kgQHMkNU0jtmrvcvBzHWbaTCTZl/+AxXuEdUhQ/psI/bFD25Lvb9Rs206tCW7QYODAbvtYq/+g3HcjkGbabBLYgMO/t0reO93r543B0eCpBrTsL62RtMYNjc3qeo6GSYojY8RFwKiNFpnEMEYg6mTyyIVp3ry1ntCeyTvDOm/GECJ0Ov2OLZyLJl45zkKcNZh6oaqrKirhhhBqwytstbEPBB8wPu09yqESJhKTO0amfMB73w6vG9dFgVC8FhjkteDVuLyPoUZ02BsMuZw3u4SWWj3aFnTEFySrAgewaNJKshCazpaoWMg2gZXV0RjwBt8U1GNdqjHI4abNzn32hlef+0MiogzNTevbXDx/FnOvfYKF14/y9kzL3P+3KvcuLbxgHvBvVGbhrW1VaqqZnvrFo1viFpBpghEbHCI7E1aam+YmIqARfBkGa0xS2z3v3m0gJZInmliCBQRFouClaVFlpe65J0CISZ1s6+oRzs0TU1QiqXFHktLC0mtp4WqqgkofAgQIhrwBnRPMGVJYz2V8RgfaOoGLQLOUY1rTFr2QecaZzymalDRMt4ZoTJHbSpMaLCupiiS+lhUTBOaYPG+AQUxejKl6HR7dLMC1TRgHGZcEqoaZS1ZsHhTMd4ZUo7GDG/dSnvJXjtDr9+hHI3T5OXiWS6/foYL585x4dyrXDh7lutXj34/GTCVTQZtwGD3Jwk9g9mE+/YDDWbCpknuXMf+BHthe9LUrFwzmP0zOJBn39VgNschPDRt4GGtGxx6ekgj7pBgL3w36YB9Fo8HU+5GHXjObxaOBknVNXmmGA2HlFWJ0hl5p0tjHTYkf3gu+CQthYC1lqZpCMYRfUimslFQuySl0t6hKCgUmdIUWc5ib4FjS8v0ik4aIGJEK01wgaqsGY8nyUzZul33Qc46rHGY1uOE98m8PLmMkaTW8679TWq+ZAmY1sucTW6VnLM0TUU1GVOVE1zTEHxynRR2SS3gvcPahqapCN6hIK1RxZDkwhAIzlCOR9y4usH1jSvYcoKOgUIJWgLBNThTU46G3Li6gWtqjq8eY/vWTV56/lme+9wzvPTCszz/uc/y/HOf5+xrr3Lz+rUH3AvuDdM0qZ+MRuzsTLBWgRRUjcPGSOM91nsiiqquk59F16S9c94hMaLRdMnIrFAEoRsduXdoH8i8QpxiqVjgxPIy3axAx+SRJBeFN466cWxuVTTjhlAaypHFNR5nDKYySZq2Bo8jqoD1yTtIM6mxMTAqS8jSNgNrKvJCY4NJatfgEBUYNRXVeIdqPMSOR2gdU98zDmMdtjbUk4pyOKEcjwnBk2WKYC0qOCQGorHYumR7c4vNzQ22bl3BVRNU8CgX6GQRQoO3DePtba5fuUIwNafXlhnf2OTsK8/z0guf5fkXP8eLLz7Ds5//NK+fO8ONq1cfdDe4J2YljX3ng4Pxu1EzpLIbxG2D+GD/4DuAfRLGbaw1U+LgkNIPx8G4mUqnJDBzfmjuwW0hh5R5jyRMyXC2CQNm72I33S5h7sXcocg3hCNBUhcvXmRra4t+v8/bH307S8tLOO/ZHg4JIeJ8xHqPiCLTGUWWU2Q5SkBi2jC5S0pRoaNCRdVepyOTjE6RNjAG62mqClM3RJ+msEplZLogywqUZPgQ2025HueTlDQlkhBa9zJMicXvEpQPftfbBDEZS6RNvTWTyYTJZIIxDSGmvSfONThn8cESvCM4u3sQQzL9jRGJAULrU7CuGA+3qSZjbNMQvMU1Faaa4OoKCR4VA840VOMx3SIjEzj76st85nc+yeef+QxnXnqJF194lnNnz3D1yhVu3jj6g8+0nywsLvD4E09wbG0FJDCabGGs3d0Yiwjdbp9M5WhJ7ztHQd2A9USv0aGg4zWdKHR8oBuhUBrtMzp5n27RTY5Vmwm2nEAwBK9Bdciki2/AkFEiGNsgUagbQ1NbHBEfLcaZ5AzZR1xUTOpA1usyntRYAR8jIXiUtDJ/hNG4YmIbmnJMbBzdLlSTEmvSXkDBEgPoaMl8Te5rfFXTxVEQkRDRwaJ8Rd3UTKohvh7hqpKgk0VpU5XUVY0LHiWB6BvqyZBerih06ief/vjH+ezv/DZnXnqRl55/hgvnXuXqpcts3Tr6k5kpBtwHMQwOEM9gVs4YsG9AH9w+RA9myx7MhuzFDDik9sGhp4eQz0wDWzIYzMbdlnpwx1vdn7Bt1b7G7c84OKyRdyx7wB3u9IvGkSCpS5cu8drZc6yur7F+4jjbOzs0pqEo0i54rZKVFAK9hT7rx49z8tRJev0eWit0nEpS6VdCIisVVTpHKLKMftGhWxR4Y7BVkzZ7No6I0M079Ls9lGSEVorK8w4ikq6hVfFN1X6e4Nu9U+31lKy8mxpOpLC6rpmUE+qmwgeDD5bGVlR1SV031HVNXdaJvHxyXptnmhg91jRY27SDsMEaQ/CePFOsrixzcn2VhV4nWSlWJbZuzdZdg5aArUvK0Q6/9W9+jd/8jV/jtTMv89orL/Hqyy9y+eJ5dm7d5OrlCzzz25960N3gnrhw/iJnXjvH6uoaa+urbO8MqY2hyLsUWUaOQgVQmaK72GdpdZ310yfS2lKWUXR75O2GaiLYqLBeYa0mOsjwdLoZ/U6Hbl5gGk89MbjGU1bQeM1SZ4GlboelxS5ZJnQ6GVpybGPJux28CNZpjEuTCiSpe3GRpQ6ESYX3kTomR7DeJfWfCQHrGoajbcx4jK8qyqqkiQ1bpsZES20qbF1TNzXWO2rjyLsKFzy19dTOUltDY2qscxAdyzkcW1jixMmTLC4tYIPFmpLY1HRdhTQluRZC3TDeHvGvfvlX+Tf/7td5+ZWXOPvyK5x98QVeP3eBG5s3uHLrAr/zmU886G5wTwz2D6+zEbddT1VTgzsmOxh5e8kp/2Bf9H5pbrbkabr94YPZJHfF4La/d8o32Pc7ONC29th3P4MDvwcvB7enGcyEDe5SxheBI0FSq+vrHFtdpeh2qZuaqipRIq2XcgcxpLWa6IlK0J2cot9HipyYXCYQQ3Kc6X2EKGn9CogEslzR7RX0F7v0+slzhdLJRYxIIkDr0iBQN+lw3rVm54BMSSpZiXkP7BIh4B3RWsQFxAWC9TiT1DNNnVwkJekq4Jylakom1ZDxZNh6s0ibhafqvjD1yt7OtENMklkgkaTxDuMSIQZonYKme1JKpWdhHSoEJDia8ZiNCxfYun4NMxljqzHNZEg0NbaaMNq8ybXLFx5cB7hPrJ88wdr6Op1eF+cd1lbkCopCY23qJ85bXPRELeRdTW9xAZXnoARTO5rSUg4NZR1waOh0oVsQM0luqRb7ZEWXhaXF5D6oU9ApOviYthpM6hJjJ5TjCTs7E8rKohe7WJvMs0VaS0InOBNRQRNtpL/YJTQObWt60ZGZBqmb1D+MwVpDU9coJWnjd9lQlWPG5ZCd4ZDRqKQJnsZYnE1qRQ94H1AERBxRAqIFA5TWY12gqiPGZ7iYIaLI85yimyW1cBC8iwTjyAjYScXVixe5dvEKrhnTlCPq4YjoLbaZsL25wbVr5x90N7gnBhymlOKu4+aA2/lncMjZYSEHSWpwx5QHWjfYCxvsXh6W/vbwg0R8WNv32jRgd+/YISUd8nN7GwZTDto9malocMgDPLykN4IjQVI6z+ktLFBVFWU1QUnytOCsIXpHiIEQAy4EjLdYAqpbIJ2CoAXV+vSLJAkoIOg8IxLxwZF1hMWVLsfWl1hc6Se/b61XcK0VkeTp2vnkIiaKx3hDWZdJJdMunFvnsY3HmfS5EBUECZ5oDbEx4BziPLa2mMpiGo81SdpKpuuBsh4zniT/b2nzcJOsBEmqy9D6cXPOTu8oObRVgFZ4EWpn2RqP2Z5MGJcVdWNxPhBba7Wp+5rgXev+JpILZCFgJxPEGLLg0cFhxjuUO7fw9eQBvf37h1KaXq9H3dQMx0NMZamrCmcMwZikfsXTWIMLDVEFJNeQZ0ieUXQKOr2copeT5RrynFh08SrSuBJVeFZWOxw/3mNhMUcrn7zE65yFpQ4+WhrvqKxHZaB0wPsaaytMCESdHNB6idjaEUwimxxFnnlGt7boh5KOGdM1Ftu0hNMYrLF4D94LLnhsKCmrbYwbU5txshL1DqWTCyyRpG523pNFSzCWYC0BUHlBQFFaw41JxdXtIcPxBFPXmMbibOorQWuCKHxwaAHB0+9qlvqaemeCIhKdpyMROxwx2dzCTY5+PxnM/D04+A5mw1ppZlfCmCa6S4m74/Gdk92V3PbCp5LNHt3clnq2sQdS7WvDAals9u9gKi1NG39bTfsJ82Bt+8q8rYEzDdnX1jcXR4KkxmXF2XOvE0mOWjOtKcsSJap1MReTZ/EYiSJ0FxY5+ba3cerRt7O0ukZUguTtXpVOFxcCk7omqEjUkSxXrK4dY+34Kv1+jyjJe0SMrddq4q7H7LzIUFl6LD46alunQa91jRSix7dWe42pKctJ+qQHkRADxjqquqGqGsqywQdobKQsG4aTCWVtMD59VgMRjDGMxyN2drapmzKtaXlP3VRs72yztb3J1tYmw/EOjamJEsk7BQtLi2itsc7TtL4Fp2QI0q6JtZ9ZiGmtQkJopav0G4whNOnwdfPgOsB9YjKpOPvqOWxt0QKdXk5jG2IM6Dx5q0IEHwXrFf2FJU4/9DCPPPY4C73lZISiNYvdLr1uH1c6hpMGi8ZliqzQrCwtceKhEywsL+DJ8B5iiEhd0YkNvYUuFAVFR6Oz5KarNg4fTVLNOkfTmurZEJLEK5bLV25BB8ZR4yTHucDOuMKXE0Jd0wwtVR0oK0MZLcOdYVo/GldQVZRbFTubY7aHY0ZlvWt0U9maG+MJ17bG3Lg1ZHNUUjcNeS50+zm9lS55FgixoWkcvjbg2o3xWhGdQQuoYACXpPDaksUAzqFjwI0q4siiykgc2wfcC+6N6eA/vRgwO/iyKxUMZobyGVGBOw22g4Nl74s5WMn+dLdFzcTsI4TBTP2tBLS/jNl2zuaZKW82arbu3YvBXjF3oMjBwRYfaNZuU/Zluy3Xm4IjQVJXr16lv7AIIty4cROlFMvLK1RVSaYzfAhJ3RYjiKa/sMjJ0w/z6NsfY+34SVSWE2LEWIexNn14MNPJVDxG+v0+S8tL9Hv91gls8q4QQvKnV7dey4MPe3uu2o8W+hB2iUxU3HW4SfT44HDBJlP0CMZ7qqahrGomVcOkrNkeTpiUDWXjMBaiZCjdQWU9RBXp8wfOpfUmZ1tLwdZEvWmd21qLc3bXI3sIHq3TJ0tiTNaO1tpdokqfFtnzIDEblo72CcTZsKPvceLajev0FhfRnZxbW1tonbOwsEJjDYoc59KXYyMeUcLyyjLrDz/GQ489wcrphwgqfXiwMpZyUqYNsZlCvEOi0Mn7LC6vsNRbILgM3cswxiMSmYx28E2FmQzRPhloGJ+ka2Ms1gaM9WQSyVRAqYhEhxAwjW29owhWFCPjGTWGaEsmteXatmXiIsOyYWIdVRPoLfTIu4uopXX6K+ssLhYEZzGjCdHWGGdomhpjG+qqwRmDlBWhHjGZjCltSe0sRabQmUICVI3HOKG2rWrcWXxIfVeyIqn/fMArTYgQbPrcSRSwzhLFgX4LeJwYzA6eh0tLAwa7m3B3DRIGuzH7E8+Wy2E0tv/q9vA9aWVwSOxtlezRJnfKcXvc3uU0brA/eH/uwcHSBvvJ50Da25q4GzWYUSPO0v6BTF8EjgRJra6ts7J6jCzLuHbtOtY6up0O1qS1HJH0qYAYk9pNZwWLyyusnzjF+vETLK0cQ+fJO4XONFme736ttshzjh9fZ2V5mU63SJJaNvWppxIxhfYDhpI+RaDaz6xHpp9pTxKUKJCpR2k8EU+QtABup1KUsVTGMqkahuOKzZ0Rk9JgHERysnyBvFhAdIdAlshC0pdefXBJ/TN1aht9MhzJ9C65huAI0aN1u8E5y1tVYks2JGlUa73rsToSdy0Op4RFJHm6fgt5tl5bW2ft+BpKNJfOb9BUll6303oe8emDmDpDVEx7lVRBZ3GFY2vHWVtfZ/3EKsVCh5hnqE6B7hSoCDEE+t0Op06fYnV1HbIC1cnItaboKKz1GK8x5Cilp95jyYoOutBECZALxnuieBQBJYFcPDpaiJ5ASFsaItQ2MCwbbo0tV0rFyGlube0wnlQElaF0H52vQbFKzLqQZ1hn0CqQnLBYgqlQ0STvKNETCehcsdAvKHJpv7Qc6HZ6ZFkOQYH32MYSnMcjFN2cXj85og3GJY/t0zVR79Naq2+1DEXW/ht80L3gPjHYL/UcEDb2DagzwQf2TA32j9GD3eH80CF4MC1tdhSfTT84mP5Aow78DG4jiP1pDiWpwWyyg/l3GzjTrr0MB8nnjvUdjBocfCJvJkUdEZLqLiyAUrz9scfTYBoCdVnRyTvtl3jTd21iAOcDLkREZSwsrbB24gQnH3ooWW51kroPJYQYWVxc4OGHT3Pq9CkWl9sNvFoo8pysSO6RRGkQRSS5O3I+YG2SbGqT9mMZY5KTWu/wwbaHwQWbiCyGlC9EQlQYFxiVNZvbO2xtTxiXhqrxNBasVzROKOvAqGyoGoNxSYqaWgGWZZlUewSyXNPpJo/uaVE97b0KwbO0uMT68eMcO3aMfr9P0X6nCtgvGbWfiz94TNO9VSSporuAj5pHHn2MmAtKg21qcl2kCUZrQOKDxvqI8ZFcZywfO8aJU+usnzxF3unS7XXpdNJg7qJncaXHqVPrPPzQOlkBCysFShyZ0vRXl1H9PhO9ROlzbAQvGuMFUxtsVeG9xQaLw9IER4wOaFpjhobGVSgdCKSvQfvgCRFsZ5HtkeHWles0I0NjPeOxx7mcnbLHqIHh2HBrOMF4g8MxNpabwyr1kfEYiZ5MB5YWcpZWe3RzRUEk1g1lE6mynOMPn+bEw6c49bbjLK0u0j/WR2XJI0fwMe1rVkL0HiGCT0YoESEqWm/oHm99SnvE0Qoj7ZrT4EDkNO52eWUwk3c29kAJdxyCZwluH/kN9pPkobmnifalO7yew8sYHBJx5/zT8g9S9W6OvQdxSNGDfc9orzQOPsQ3BdmbVtIXAWMdx0+c5Muf/Eo+/rnn+P+pe9Ney44rTe+JiB17PNOdcmQmmUmKFCWVpCqVauiuMtgfbMOwgXIZBtoNGDA8tH/HcTdgGLAB/4JGw0a7/dF/IQEDrrZKqq5qlUQqSSqZzOFm3uncM+0xBn+Ic+89d0hSVSLMZCTu2XvHsCLOyY1Ye6291vsuFgs28h5aKWzXoVQUknYjjUSACLQYN9+4S5ak5HlG3TTMZ9PgImsqbu7scO/eXUbDPlp5Eh3yqpSUSBUSrLzzxEmCEdB1ls46qqqG1qzAZBX2xGrRmjgOMnAurEc4hAxY6d5LvNSIWDJdNjx69IQnT56wv39IURRkaUZRZLx17x7b21unFCJeVAF7UEo6azDOYbzDKUnmLd53WNvRdg2dFdStZVnWPHv6lN3nL9jf32OxWJzCMAGnVuQJZe+6QjopQgTXzjcC/nxVus5wbWeH9779Pj/5d3/LfHLMdpYTywjfdjivcGh8GhNrDSqgkd998y55rEl7fUohKCfHmKrBVjXD7RG3br/BzZtDYqVII0Uza0k2CqQWeOOwZcOdQUtbN5Q+sOaKtqO0HhVFMK/wziKVokwTkkQjEETCI4zB4ilXFquXCqEkUS45fvycvaef8vmTRyyOl3jl6Oc5g8EGt+5+i53rG0RxjBcRVtZIZ4njCBFLWhym60jamkIYWtcSSRsIQbEsypr54ohnHz3kxf4ez569YDlfYOoS19UI77G6CISYK5x/5QWmM8RpTNs5nPeIzmG9Q3HmNn7dy3jt7JxFsLKOwh9n7r4zu+WSVjqzLsYXklYvdOTi5fiKbmvruLi6s4+zXid15+Zdl3TFGi4u5lXNXzzq/Ljx+ul4bd3rau5krax+q69OTb0WSirNcpq2Jc0ybt+8zYvnu2zsXA9PcyukAOF9YAVdWTsCgU4zRltbSBmsr8nhIeVihvCO2zeuMehleNdhu5pIKXSkgqKRKqDRwoodtQtcOgisA6GCKxCpcHiiyJ6GkCshABdgdQQIF8LUpVAIpcAKjmcLPnvyjA8//Ji6atA60HYUeYaXCcYpNjdHZDEkRYzABmI8awGLsA7rPVGc0FmLbgw9oUHFnBi/Xdcyn82YTqcBfcOGR9yLLr2Ligo4dQ+ecJF8U9x9aZbTdS1ZUfDGnbu82NtjuH0DryxCdYg4RSlBnMQIZwJKifGIKKa3sckN73m/7aiPj6hnM3zjufHOTbZ3Bvh6wfyoCgjjSpIX6erBQ6ASgTOWhUmY1J5EWiqWeBXjgTiKQHik81i3ohtDYIQH0wW38sokkSiiJALXcTyd8fjZ53z4q4csJi1xL0bLjuFoSEVB5wX5aMRwI6PsOhIZwHNFF5LIozjDWcGxWoL2RAvD5maAE6tLC01D5xw1isnxgqauqJs2yLEeKwxJqug6SxqndHWLl5K2arBKEicR3nm8tzgvAhXJN+NWOXuiP9MyrG/4562biwpqvGYRnHRZl7N+/AK1Nb5wvpJxhZFyfprx+gWX17Im9Kz75fbTtovrWBd5ldhXlPHpTBdnPRNyEu7+FRpSr4eS0nHE9PgYHUX88Ic/4C9++jNM2yAShV7RYHsX4I/EKsqv6QwKgc4yNqPr9IuC5WJOs4IISrSkWkwpF1O0SkjShDRLieN4RfUeAgjqusWuXHxN11K1HSrSIYw9ilCRJo5X73CkBCXx3uKdCRYe/pTGI9YxddMwW5Z89vlT5osapSJa66mamrLuSB8/Y1E1bG2O2BoV3LuzQawFSinMStFIaTHOItQcYz2JA9300ckKzNZa6qpmsVhQVVVgVY0U0knsCTnjypDynL1vWy/e+9VTNKuN5/XffeI0YjI9RkQRv/ODH/CTn/0V3nWoWBNHCq1FyKnrbLhXBFjnUEKR9XvEWjEqNMv5lGpRo4Ug62sWh4c0laffT5ECtm5uoqRCCYXrLHme8OJzMFJgyo62nqNSTRTFoDR1HKGiiCSJiYQIYMVe4FwHyqF8FH5vFYFQyCimMy2Nbfnbn3/K/nFJkQ+ZL7oAfGyWCD5jenTIjTs32doccOfaCKEDTmUkDZ0BYytm0wbvBDqtSURA81dRHIIldExpYb6cg61RSpL3ewHvsu1CBGJr0FrSORuYAsQJ+4wEEx7MUOr09/xGMI+Nv6hyfH7jHrPSSWPOQrnHVxpJY86sh7OGMwtrvNbr4rhXWSbn+lxxvKr/b9Q0PtMVlyzA8bqCGnPuc30x5yYYn/+dTseurzj8fcU66vV4JyUEmK7lpz/5f7n/5h3u373D7uPP0N4gnUUYg2tqnOlW6UIqUCtIiVcRMk4YbG/zxltv8fZ773H37fsU/YI0S+n3exS9LFBzJzFRpFhFaYfoPW9Pw8e7zlDXDdP5gsPJhIPDY2azJU1j6DqHMY7FsqSqW+qmpTWWZd1gncQJRWchTnM++fRzjqclSVrQtI6iGNEfbqGTgmfP9/i3f/0LfvKX/46f//IhH//6MQdHUzob3rMhA1iqsZa267Ari60zHVVVcXx8zPNnuzz67DGPHn3G7u7uilrEBIW5spJOA0RUtEpelivlzCmU0+n7KOe/ETxB4DFNw0//4t/w1s2bvHP3Ni8/+5TYVSFPqGyCO8saIqVQKkaqQIXhI01cZFy/fpM37r/PvW//kJtvvkOc9Yn7A/qbQ5I8ob89QEeSOI1JY0kvV0z2Z/hYMCgiYpZEssM6y3w+Yzo5ZH//kNl0RlnW1K2lbSzLpqaxNuT+NS2zqqYtA2STcYLh5ga//KsP6eqOjcGQyRxG/S02hneI5AYv96Z8+vAhf/t//yUP/59/w6NHD9k/nlG6CC80iTSkUYdILU44dByhhaVua+bLBUezCY+f7/Lxw8f86i8/5PDJZ0xnJVXVUbUh7054s3onq8kiRZIqpJZEsca2jmrRUJUheOnEzfdNeHd5tkOPz1WFw/is/WL9Ff1PW8drDettFy2sCxbPVVfn6y4YSuOr+1we8SVlvHa4uMYv05jnrk/WMz77HK93PpN54ka9pOh+y/JaKCmtgmXz/NlTtISt4YC//tnPOD7YR3mP8BbhHN7aABuEJ47UakMP3FGBQsOH9zl4lJRoHRHHmihSKxfMifcrBEl0NuQkLRcLlssl1jms9TR1S1W2TKcLXrzYY2//iOl0gXOCctlgjMMaqJuOtrOUdUtnPMuqRUYJs/kSFccYD1nRZzDaYrSxRVb06axjWdYcTqY8f/6Sp09fsLd/xLKskUojZbTCK3Q0bUtVVVRVTbksmc/mTI+nTGczqrIJfEbtCQ/UmVvvZCM5wRk8yZk6cfGdBBkIz7m/171EAgSO/b1dEq3YGA756U9+xv6LA5IIpLAo70PeV9cF7EQnkXGEjyyt8CzQNCrCRhYZeyJjKCJBniekmaYzhroO73eEc3RaszQgWkN5MGE+r5nXHa1xNNYxKzsOD2fsvzxk7+kB08mSZe2ojaXtLDhFa0MUaN11lMuaZVUT6RRLS+01S69I+xnbWxtsjHrkg4yyK9lf1LyYz3m6P2Xv86csD/YxTUUnExoSWieIEMwWNfNFHfLzmkD9Ui6mlOU8YD1i6CRIrULqwkmKghAICPiSXoTACGtpm46oiNC5Js010nuwfgXe/HXfBV9exlfskqdWxTllsm77nFhEp0IuH08srXOiz89zXhleXtnV6+WcZXJ1r1X7hQVc0IlfqH+uGnQy76VZ1yHfnXUAACAASURBVOcZXxS13vvsdxtfJecrKK+FkrImsOO2bYXEsTns49qaZ48/QwsPXYf0bgWyukq+VYGA0AGGEMl1glRuXHhSjCK1siZUCJgQAD6gktuArdeZjmVZUlVVsKSalqbpaJqOw4MjHj78lI8+/JhHjz6nbW2ARPISYzymdSipKcsmoD4YR2cc12/cQsoIhEJIFZRN3dA0LQgZ3InOMZ0t2H2xx97eEbN5GehIIo2QAcIm4P61lGXFbDbn6OiYg/0jDg+OmM/nLBZLqqo6F15+ktAbAiPOaENOFJdAnIaoXxXt9zoX7wLFRd2UqAiG/R74jpd74eHG1g2YDh2J8B5T+DXkEYdXq6PvcD5wfalIkGUabwKYb5bHyEjihWfZGOa1CVGb1rL38pjFokREkrKuKauGZdWwv3fIw4cf8/HDT/j8s8d4CEgj1mOMW7kONZ11dNbTGU+5aNm5/gYyy7HESKkou455U7FsW7yOIcpZWsHLecOzvQkHuweU8wV5vyDu93EqDe5D6amaltms5ODpLnufP2F5POV4csx8MaesShbLJa5ugqvau5NXsiSJpus6lsuGujYY43HCYzpDFOtAJolfpUjYkKv4mpfzW+jZ8arak/qrIgHH5z+u0AgXxJ2ba3xh3vNzro+5quflmc7Xnly9WkmNr1a0FxZ8+SuNOW8Ojc/LWvU5N82a8j75FS+J/S3Ka6GklvM5bV1RLedIPKNBjz/6gx8x2d+jKReBTbbrwFq8s7gVZYZzIQLJ+kAb3q6oGpx1iJOcqWgVMKGDRcUqedauEl/brjtNom2ahuWyDIEUTcfz3T0ePXrChx8+5Fe/+pTZdAEiAhTGgPfBpWQttE0HKNqm49133yNNc6SQdMYwmRwzmRyzXJYIIUmSFB3HWOuYTGYcHR1zfDwPT96EnDApA2wTCJzzIXpxvuDw8JCXL17y4sVLZrMZTdOeKqGT8P31cqKwhLhsaX3Tyux4Fni/ygXCeYaDnD/+w9/n4MVLqmoZqM69xzYN3jTYpsK7FpDIKMY5SW06qqql7gzWBFdqpCVpLMkSRaoUCWBqC7GiW5YkqaBrFkhaIu2Y1Z75vKarWpqq5vD4iGd7L/nw44d8/PATytkC5xR4Rd0I6qXHWxXeIzmJVAnGO96+/w6bvR7aOWLjODw8ZjYtqRctsvVEUYqMMzpjebE35/nzOQfPF5TLjspFdCLC+QDxFSCgBE1bs3+44NePnvHpo10ePd/nyfNjys6ydKxyAh2tcXjrcbaDRCAzRZbHxLEOoM3OIazBNR2RBCF88EJ8I+6d8boRwMUtc3zh4mRPPqekxldt4F8w1/mhZ+fji2tYW9h4ve/4lTIuta9NOb6y8ysWdGktF7usSR3zCrfe+pjx6QLG52u/Ui31WiipWEu2N0co53n+5DFaev7kj/+Ib3/rbZrFHOkMtimxbeAIctbQNXXwlQuJFzIgSgOWwMqrdIROY+JUEyeaJI3ROgL8CpkBtA4vvIteQZbnp0jnCEW5rPns0RPKsmXv5SGffPwZv/jFx5gO6sbSdQ4hNU1tGA23kFKDl0gZsbV5je+8/zv0egNGww2UiogiTZomOGcxtgt8Uy64DZvOU9UtZdXSto5gMHq6tqNpWroukPkJITCdo1zWHE9mNHVzGp23nszrvQ9kjWsWFAT3X+C7cucsrm9KQm+Waa5fG6Ks4eWzT4mF5Y//4Md868171NMZyhl8W51ydVnrKJsG3zUEWAWNR4WcIBcAZWWa4FVCb2NAnvTIopgsScF66vmSSECsBU5DtFmg8wLvWnrthI2+oO0MH370mP0XS54/P+Thr37Nh7/8GNt5qib8Hyip6TrHsL8BVuJbg7GwuXWN7773Xfp5n+GgwCiFiySRVnhrUa5BmwapwAhFGUmWoqNdNFA5lPFIEZLIuy7gN0Z5j6TfI0pTurajms+RokUaS64dSRQUebeywJQw1NMZvguoG84GWCljHKYL77qsVXgRhbB6+frfJ6GMz47j8TlX3vrZmC/YuC9Xfcl0V1gRF2Sckzm+POv4wvHcwEvCT6a9OOp8p/U5L8m9JG187rtcGrkSFpZzeUGno754or9TeS2UlCBAtGRZyoe/+AUHe3uYtuEPfvR73Ll9kzTWuK6hqcrV0/KJxeDDhiNkoIw/FSjWkjsDQ++JpWFPNm5B4KfSiiwNUX9xHDMcDEmS8PTaNB0gkSrGWs/jx09pWxMUiQ/Z93aVkR/rBGMtQgjiOOZb3/oW77zzLfK8oG1bmqY6pe/wLsDNaK1Jspxeb0Ce94Ib0Xo6EzaesgpEjLPZgsWipKraU3DQKNIra2vtdxSCKApIFFGk0LEmSRKSJEFrTRStgijkWRDFN6l453BdR5ql/OIXv+DgcIIxlt//vR9x+8Z1siTG1CXG1FjTIAVo6QKeXmepW4fUQZbEEZ3kkxGUlhQKqTVV63DSYTsHIijzKFNkhUbGgkR70nyLWo2Y1orOtOhUI+OM2sLjJ09omxpjPQKNR2AMSCtI4iTkIDlBnGTcf/s93n77XYrRFt5afFeDCN4BZw1KKqRTaJXRy/pkcY7xEaWNWBrHrO6YzSrm0wXTvQnVosRWDbJryKQhSzT9UY7WIL2h8RpDhBKKOEvp0MR5jyLLiaIE4wQIjdIaISSxDpF9/jTF4Wv77/+NyyWFNOaCC+vC9Vn3s1Gn1ZcaL8y13udC34tDx1dbJ+sb+0n7+Kx5faKLpxcqrpDJhe9xcewryvjS77K2sPH6b3x+njGXqn/r8lrsVFKq4Mapa3afPePJZ49JoohEa/pFRqwkbV0xn02pyjLQr6/w6k6Ycv0Jrfvqb8U5eIrD13YtdbvC6POB3hvncDZQwTvnkTKi1xsQRZquNTgHTdMFJGwZ8XJvn84Scl5kRGdB6ZhlVaMijTWerrNsbu1w8+Ztfu9HP+I73/kuvX6fE0DZSAcsNSlDeLiONUVRUBRFcD+2AeqmLCus83gkZdWwv3/IixcvebH3koODA2azKW3bntJ4gEcpSRwHiy1JE7Y2N7l+/VqAhRoNKIqcSCukFIhzb8BX4Y6veYmiiLKsqeqW509f8OTRYxIh6ecpgyJDOU9TV8znwS1o6oa6aqmNpLUKZ8HYgAjhRaBxUUKgVIDAak3Hsq04Xs6JCx0iLld8ZFgJBIQSqQeM3vwWyB7NosJ1nnJZgwhh3/sHh7QN4BXGS1oBKtNMmxqUDgnbOHqj61y/8ya/+/s/5lvvvU9e5BgLXkjyQUTcS1CpRvUKsn5BL8/JkwQrBa5dYKs53lV4W6MF1GXN558948njJ7zY3eXoYI/jw0OmR3OWiwYC6TxprOnFiqzo4cjZunmNN25fY2OYcf3GiF6hEZFCxoH88+RdrnPfjBD0s5Dwi/bA2dV4re4KjbA2er3+qrkuSTsbdXHo6eTjtZ6r/mPOjzlZ1sn5FWKuqjhZ7yvXPb7y9NzIq8dfHDi+dH72W3zZr/Z3K+J1eD/xT/7z/9QHJQFtXTIabXD37l2GoxHf/p3v8+njz3n68oDDuuPO2++RDkbU1iPimCSNAxOtMQjrkEAiBTEdrlvSVTO66gh8YLrFOyQSJSOUlMyPj2nbEPggVEwS93j46WN+/re/5NmzfZrOEGmNThOqquLf/w//A7773fdJsxSpBEU/RQBJ3AvgnNazs3WDPCtQSmE7w09/9hP+3c//mg8/+gUvdp+AcERCIIXl++/e57vvv8ubb93B+pZlOccYi1IaHfcDCC0Rs0XD7u4+uy9esn9wwPOnz3HWoqTAWfDeoXVMr99jZ3uLJEmIomjl3jPhxbe1HB9PKcsFXWOwncQai7UhYfPlwbPXegf6x3/+ZyFtRwuqesnm5iZv3r1Df7jJd773HR5++hlP9w6ZdY43336bbLhBg6IUWYj0VBbtSnzrEcYzSDW0JYqGcj5D+QllWaKkJE4kTe1IZIKUEdXRS5y0TGcNNQkbwy0e/voJP/3JX1Av5jx9PqO/XaDSiMW04T/6T/5jfvD97xDHMU55skEW2KFVgXUtXee5vX2bOM6IdIRpDD/7t3/B3/zq5/zik4948tGnWNuiIkmWSf7w/bd59+7bvP3OW0i/pF2+wLYOYyP09fvMa4HUMYvplKdPDjjc3+Pz58+Y7B2GhzZniZIMbx0SQTHsc21zm7ifEWsJTUtjLZga4UoO5p7lsmR5XKGSHNu0tI1BCsfLo+ev9X0yHuPX1cuY3/DB/kLH9ctwfkHxjcdnf5csld941rX5LirFtdlPxP2mYscXjhebx1dYZJd+gPGp5fTq7/WqBY0Zj7+aJ5roqxDy25bjWcmbb93l7q3bfPLxR+y92OPZk6cMhgOqpuHz3ZdYqZh7hcCxWMwxSpMlKdYYNEE5CakCQ6+QwSXoQ2itWRFNSR+SPKWUp5TdUio8AQLJe8FkOuPw6JD5bEHdNsRxigU6E0jmDo8mtNbTz4vVE3kUXr4nKbEM77LKuiXNeuRpQW875x/+yZ9y7+17fPfT7/Lo0a9o6hLnOqTruL05ZGtrgPcdTVvhfYBass7hjAlMwTiMgaY1lFVFVVUgHK1p0Spi2B8gZHBxaq2QSgTMvySmHxcB889ZyrKiqkraVmKMQAuNFBFS2vCi/zUvs2XF/bff4u4bt/noo1+y+/IFT54+ZjgYUS9mPNrdw8YpjQWB5+BoRitSBtdytHJIPF3j6WU5bmnAx0S+xjhoLcjOIaVGYOkag9ZxCMLBolPNvPa4KCZWCUfHR+zv7dIsW/aPFhSjQbDKW49TgoPDI6rOUfSzkDbRRngpifMUSc7MVJTLllhlZL2cuJ/xJ3/6j7j/7fv86MXv8tHf/BUlS1rZIEm4r/psb/UwtoLyENcapiZB5xscHbXo4TbVck6mJFiHsU2A80piJkdz0jwmTYKLOIoESZYhlUQjEVah+yP6SUMR5xxNYl5UDSruEL5CaYX0Cc4JhDdf923wG5RXK6iL1+daVrv2+Fy/8Zm88fgViBDjtfEn/c9GX5jllfOPL7WPzw5XzPGF2mp8/nR8ofG8u2587uy05rIWu2LO8ZUrubr271deCyWVFz1UFAUq9aahbRoWyzkHBwcs65oOST7aYPvOfSIlaUwXXCtiFa0kZdik/co1IyTOdThkyJ+yHqxFYANhnAruLed8QEw3hhiFdXA0OWAymQQqd++RStHZAK4pVYRzBBT2YkDdLFcU8YZI1QihOJ7OqHRHnhWMRpsopbl27QaD0ZBbt2/w7ffuM50dMTk6pJofc62fIwgKyhgTGINVhHOCtnM0psHYlqZhFSLfULc1KlKkSUKapGzvbBPHcYholGKVtBxRFDla64D91zqSJGZzcwOpwE/m2FYGQFEBwr3WD8cAFEUPECymgRnXtS1lueDw4JBFWbNsPRu3brBz/U2ciDHWEvckUthwn/iA5SdVhEwFnQGhdaBBkQK8ClY5Fq08OB8eElYuOKFjtHNYB8vFnMPDA8rlDCssmVaUdYcBHOHdYqQ0g3zAoqww1uE6g1qUKCGZLxY0ZUeW5wxkRBwXZGnKxsaQa9s7vHPzFkd2n735Cxb7NW+6hKKQLMs5UxPRiCE2z9DFEN046qMD8sJTThrqsmYxr5jPFiRxxKDfI8oSdq5vE+sEqQQ6itBC4rQn74e56QTLcoHqbXL7tkGahL99WYWgnQh0opGvxxuCLy/j8Vlo9Lltf/38CoVwWRCnyuGcUlqTOF47riyPc9LWJhpzlcV00nDW6cSAOVvamLPRq8/TMeO1jpfPL8x0oWZd7iv6n1pUl1c+vrI/a7/9b19eizvOe898NuPp06fs7+1TliXee7qu4/nz50ynUw4O9rlx4zrCe5JYE0cS4ewK0UcgVISMQl6HUAovAuuoR9JZF3KoOhNAVREhVNm6QLmuJDqOUJFisZixXC5XmH0K5wMcEx6UjNi5tsNwOKRX9EmTDNO5kLpFQF4PbktFkiTkeR4I86SkyAtuXL/Ju+9+m3feeZfbt99gY2OLvNcnSXMiHeOROC8AiVAa6zyLZcnRZMpiWdIag3Ue50HHMUmaEmt9GoRR5Dm9Xj8Q8dU1dd0gpSRNU/r9Ppubm2xtb5PnOc4FpA2kQCpxSvT4OhdjLLPplGe7zzjaP6Q6XkLnsa3heHZEZ5ccvvycO7e2SVPFcJShVSDv8z4EjOg0p60DGKxQEagYYyxJGrHsLNOmYmY7Fo3FiWCRSTxKJuAD9FUsBPsvX1KXcxwOoSI66zErMMRISG7f3GY4GNLf2CTpZTS2o3EdSEWHo7YdMlHoJCZNE7xrkXg0MXev3+XezXe4O3qX2/373N3eoT/YoCoFncipdIaJ+8RJxmJZItqW9vA5tqt4uiyJt3KMNDQ42i5CqgIvEhqVIIhQsk+c9sgTgV1M8O0cbxxSJuj8Gkk2ZKdfkMQDqsav/Q4OqV9/ixtONsnxydWF46vskKue/1c14wvnawrrFGmBteb1GS+1XepxKu9c2yVLZnz6eTZ6fKk9DB1fqB5zrusVcl9dxqcyxut1Vyjb8fmPr6S8FjvTp59+wrNnz3n58iVHR0fs7e0zOTrGe09VVixmMyYHh/SLnLYuA7EcHmG7QHHtBUIlyChFRhmoBC9jkAkiShFKr6wqwYo6KrDyOodd0dN7PMZ2LJcLurZZZeUHqCS1iorDC3784z/k9s036PdHjEZbTKdzpIjYuXaDmzdu89ab97l37z6D4Yi6bmlbg9YJWVaQpjmxTun3hty4cZu7b95HRgk6KdBpgUNSNR3zZc1yWTNfVuzu7fPJp7/msydPmc4WgSYeEULunaNuGo6OjphMJsznc+q6ol0hVYBnY2ODzc1NpJTs7e3z+LPPKJclvV4R3IjYFen66w+L9Mmnn/L8+S67L/aYTI94sb/P8WSKs5bF8ZzqeMrBs0N0klMezxCNQRkX3vG5ltQ1KKFQSYFXOTKO8UoTRTnSxSilkTpaocMHNAcB2ABUhZAGLzqcMFT1AmMsOi9wTtF1hl4MvVjQNoJ/8A/+Pd7YvkmW9dm4dp35vCFKE25cD/fJ3bv3uXP7Lr3BBo0Bp0BkKb3NEUlekAwHDIab7Axus9m7Sy1TZG8LleRIJbFmSVVOaRPBwXRCkgge/F9/zaOncx5//hKjNLUs8DoiNRM2zRPc/ClNfUhVTWmbisNZAz7Cly1b13bY3tkmjRTHLw958WSX48dP+KPfG+JMS6cERkka9/W/w/7yMj4NngDOLJ1zPc6fjFfmy3r9qcI4kTVeH7B+XJd82U65dDG+dHG+04V1nEwzXqs8ETG+UsYF/Xa+8+XyqvovaBqfLmh8rucYvlDe36e8FkpKSklVV0yOjzA2POVqHZ8+8esoYmdrm8V8FuCQjEF6RyTAW7uSolYID3J1jBBKo+KEJO2h4xwVxcjVuyfPCrHC+xW8ksM4S5qnFIOCKNFY162YeMHh6fV6bG3skCYZcZxQ5D2iKCbSZ3KVVGitcdYxn89xzgWq+RUvFUKgdUKa5hS9HoPRJjrNVs/rEhXF6CQh0nFwKy0r9g+OePHiBZPJhKqq6TpLXYc8Ma01vV6PNE1XYeWCfn/Ardu3TlEnRqMRb755l7ffvs/29jaRjrDWkmYJUaQw3tF2rz99vI4kddMwmRyFMG4ZoeKUolfQmZZIKrYGm7RdibDQlR0CSCXIrsMSYWSGkwkiivBSYUWCi3NQafj/JCNWGYpAFOgMeOOx3qMiBQS8Rhkl5L2UKI9wtkX5Fq0jlkaQJX22N7fJegOSOKGIM6TzRComiSKUFyiviHWMNZbpdBZYhbuOsqzoTIcQEClFr18wHPXZ3NqiP+yvgF8FOIlWGrxgsah4edwxl7D3+efY2QzflFTzhuPGM1UJrtAMow5nI4RMaIjZuHGdzTt3MEoh2obrN29z5603uffuPQYbI2Ss2Hs5DSzSOsb44H5+7csY1kOl12F7zjqcPz1nDa3qT8eNVzbD+KSBix25uDO/0sI5//Hl5XR963rxwlrPNO1vltt1cewXjLnUtLaeqzXwV19eCyV1wjwrlQzEfZFC6YjRaMTW1haDfp/RaEC9WKCFII0UWSTRwgdEZ1Z4dEhYbfZCRsgoJopT4jQlihOkivEorPGr0PNASa/UCbCfZ2tri+vXrzMY9FdrEtgAL8E7b79DFEVIGaFUhFKaO2/cZWf7GrFOUVKjogitY5IkJc8DXX3XBTSLrrMkcUqWFcGySnL6gyFJmq+sPQFSEemYOM2IkwypItouROVN5zOqql7lW7mV+9Ku8rVsIGdsW4SAJE7oDwYB/gjo9wfcu3ePH/7wh3z/+9/nO9/5Dm/ceYPta1sU/QKdRF/vTfCbFCFACpSW5HlGnMYorRmNNtnZvka/GLK1s8ViskBrRVFE5LEiERaQtDIGGbjBrAWhNF4qZJSgVmSIaZERKY0QEV0b8vGED/l1zoIxHusst25ts7WxRZGkp4naZdXhreJ733sfb0Igj1ISScTtG3fY2dghjgukihFCoqSi38sZFAXeCKqqxXqPMZ4sLRiONujlfXq9EcPBgLRI8InCphISiZMKLRUqKzgsHZNmyZbYZ/rsU+aLCp+BpaXyluNWgIpJ8jTAQrmSbtnijODWvfskWlN3hl6vz9vvvMe7P/ge7//RH/DdP/6A737vXbaGfTY3e8TxN8Ddt9rRw2HMZatlfP7yZMjFXX41dN2ptda6Zu2cdjx/XO+5fnkimIt1F6uuqINzCuuiiEvVr+j3xR2+oIxfcfzCRfx2RY1f8UP8/1n+5b/8F2O8x5qO0XBInmX0+z02NzYo6xodxyzLks2NDaJIcePGdZJYh4AFHeOFIooSpJAEVSVXjOweKTzetTjTYboOawzOhvwoaz11W4MQK3gky2A4IkoSyqrm6HiCXdF56DjlH/8X/4T+YEi/3yeOM5bVks2NTdI0o9cbUBQFUgaFm6UZSZIwm01J05Bca7qAZG6tRUlFmsQhmk9A1VQ8f7nLfLGgNRYhNTrOqGrDZDJlNl2ECEMb0DKkDO/VvLV0XbDU7CqZWAio66DMguJqThN9i6JgNBpy49Ytbt66Sa9frLD8BP/0v/nv/oev+174ovK//2//YqykQ0jH1mCDLM3oFzmbmyMWsxKIOJ7OubE5Iu8l3NwaUaQpxlmczqi9QkuNcoYk1YEg0AHCEykQrqPtWozpsJ2laS3GepyQVKYNOJEm4Ehu3bhO4yOODpZMqxmd8zTWkEaa/+q//i/RMqc3GFAkCWVds3N9myxKyLOcIk3QsUbHKYM8Jc8Snr/cY9TPiSPFYlHStBXWGrrGBroarXC2o3UlewdPWCxnICRpFhOLjE7CwdEBBwtP1b/B50ctxhr6cYutQ8BQ1XbUTUhSjnUA4XU+cG4Za6nmcyLA+oi4yNm49wbF1ibXBptsjjTIAEb73/+3//S1vk8ePHgw5sEDHlzRNoaz+rUOD04+HzwInT6A8QfwYLwaNX7AgwfwwdqwB6fyVmcPHgAPGD+AB+MxfPDBqu6s8/gDeHAyx7kFXljtqn08hg8eXGq96tucihmPz6YNzasvdNr3inFfWH9FebDqe3I8lfBgNdOYDz7gK7lPXgtLCqCqK6IoOo3uS5KEWKcUaU4SaTZ6fbzp6KqKl0+fcHzwkghHLDzSO9q6DBF5K0y+QNYWWHyl0jgvaI2l6SxV3TGfl9RVjXGeRVkFGvfOUJYlRV5w//497ty5QwCzFWxsDKmqCqVWQQllyag3AARKaeq6Zj6frxQFdF1DVZUIPG0XQGKNtcQ6PmXFFSJEmhkXUCaUjOj1BwwGA6y17D7bxbYt/aKHtZamqmmqBrHKe0nimF6vR57n5HlBmqYIIVgsFkynU2azGRAU1u7uLp8//pyjoyO6tkNFiqLIuHP3Nj/44ff58R/86Gv9//9NilSK+bTC1jCZLlksSqI4IsmKsPnnMdd3hjjXUS7mvHjxnOnBC6Q3aDqyyNK1SzprWcxqrBEoQBqP9AqBwllH03U0xgR6lLalbhqsVCzamrItaU3L8dGCO29d54d/+D1uXLtOOVvQ1TWj4YDZ8YIkj2iakuNFQy/p4w1IqanbimVd4l0gtizbmkVVI+II4x2zqmFRt0ilVuj9iqToIVY4fc5EJPGIPNkhkwXmsGbv+SGxbdjKUg6riN1PK6glsonQDWwkmo1Rn2TYxyOJ04i2Ubi6pZrNOTp4GfLm2opHTx/z9JNPKJf7dMtn9JOGnWub3L7zJj/6o9/n93/8u1/3bfD3K+PVYXzycaHh9Hx8djgZs3L1nVpU45Nh4zOZF919V5k76zLHJ/OMLxkf47OFXhEl90VrP5F98RuNz/d9pWFy1vt83/Fa+1m/y2tYb3nVHH/38lr4eG7cuMH+/h5ZHLOzvY1Zvb9ZLpenodVxrEO+vDP4tsEp6JYLhAURh6CEJE4CKKeQ2E6E3F1jaJ3AWFYUGw5p7Ir+XYJSAZ26NSvGX4hTKPoFt964jU4yqqphMNwMyse0tF2DUoH2YF4uVjBE0SlH08pziNYRg2EfIU7wAl2IzrMrlPY4hqVEqSgk32pN0wZqjrrumE6PqcoQM9bv9+naFmttiESUMXL1Dts5R9e1dF2LVJJ8jdyxaRqqaokxBq01ZVXS7xcMhkM2trfDJp8k9PvZ13gH/GZle/sG2F2KIuP6zg5dW1M3dbBWewnSC9IkQUUCbAumpVnOUHFgWlZCkUQpSaSIshwlRAi80TF12YY8IBESnL21KGNWDLYCZwR12+IJ9wpO4KWm1yt45/03uXFtyLKs0fFW+N3bCp1qbF2SJopyMUUmCZnUWNNhXUBqdwk4YxkOByglaesGiQ15e6vctTTPWEynCKtIZIJWMcfNgtqCdB2z48PA+hvnpGmNzBvS1LGxkeFKi/CeumpRQhLHgraZIiTYOqI3yCFP2D2YoKQh6ytmkzs5wQAAIABJREFUiwUqapjNW0Zb19hIUvqDPknrGb5TfM13wZeXK91hZ9qB9Q325N/FTfd0+PjEDXhR3sn4q2VevY6zficyz2ZftY3Hay7Kk7oLC/qCMr50XJ9zTbeek7n2fS4qxSsV2uVZzma7qFR/+/JaWFJaa3Z2rnH9+nXyPCeOV647KRn0B+RZhpKKqlyymE1ZzqY0yyVducDWFRpHLEB6h+86urrBdB3O2oDb1naB/lpqlNIgFNZ62s7SGUfTWprW0HWWxnThydla+oM+129c4+at62xsjHDe0DbNykrz1HXNcrmkqUMSrvcuuIqsQQjQq4TaJNFkeUpRZAghaNqWtmkCGoQNsEYn1Bnz+YL9vX2ODg+pqxrvHFmasLUxYtDrkcYaJSCSIrxLg9U7qmBBRjJCa43WmqqqTunlpQzuvqqqODw6Ym9vj/29PSaTQ5aLBW1Tf813wZeXKI4Zbl1nc+caSRqQILyV4BR5UpDnBc5JqjLwb02PZgEW6XgGxzPSuiIVHiU80nbYrqapWozzVM4zr1vqxoKIkSLGWblyCQd6C2M9xgSHsnUdXVexmCzY2d7mjTfvcuuNN9jaHuII95LpPCIWzKuGw3nJoqlp6pZl1VK2DV4YED7kMvVS8kyxtdHj2vYQYwVl2dG1DYuqwnqHSAMlfScshorD6XOOJofIyLE8rugpxb2R4ltvxvRGLXPTYLVERgrpI0wtaKxiaTyohGKnTzbMsXVJWy9Ico1KYq7fusbh0xmLJ5YXH0148tlT6nZOVc2YTaZf923wpeW8ZbNu/Ywv7PGn6uSSNXSqvk7GnVMY52Wvb8thivHZOi7Md/Va19c5Xuu5NudVVs6ayPHa5fmvMr7Y9eqljLkiIOOiyr5i0AVNd7HmqyivBSzSf/bnf+YH/T46UjhjgnJpWpwxAVbGWTrTIpQk7/UYDAb0BgVp3sMnOaoYsbF9hzwfoKIML6MAviot1lYspvtMj14yO96jLZe0yxLTtERSYryjbJZ4LFEUBW6dulqhQJvwfkhEpEnG7dtvce+td7h1+w5ZWtA0DcYZil5Br9/DWUvbdqSppshy0jQlUoo4PgF2Fey/3OPo8AghYGM0YDGbMDneZ3K8z8NPfsnLl88py2WYU/eJVIqzMoCIzmZMZ1OapsTZDinVCorJ0uv12NjYYDAYrN5JVWRZeC/mnEXKALhbliUeR7/fp98v0GlCEmlQkv/pn/0vr3VG75//+Z/5jdEQ4QWmbQMyR9dh24Yo1jhrsaZDaUVa9CiKHsONPlmWItIcVWywefMt4qSPVClexCBjkAYhaurjXQ52d5nND2gWJe18gbMdOpJUraOiQbiWBIGtWqZdEygvvKN1HmsViY65d+9t3rr9FjduvkHS61NXDVKDTlI2i5zOGRrrSRLBMOuTkJDnKUpL4jhBKHjy9BmTwwnedty4tc3R4QHTxQGTo30+/OXfUi73ArlmlCBtilcJygnq+ZKj/SkTW1IuS6JmAiR4NL4xFL0hve0hmze2aBY1rSnpZz2GsaTynjZKUA6MrVHekOuCYmcTLSR5nOBjyf/4z/7n1/o+YTz24/VLrtqqX93jnKhXKIfzUi6P/8I5x5zTYOdnWBs1vmjIrMa8erlrltgVXcarjyutowtz8JvYReMLfxfGj8dfyX3yWlhSGxsb7B8c8Otf/zpYVdeukecZy+WS48mEpq7Ae9I4JtURWgLGUC/nLI6PmB0dYJsSiSPRkiKOUMKHfCcPaZJSNQ0v9g54/PlTnj/f5WgyZVk2zBcli7Khbh3GCurWBNbbsqI1gTpb4HHWsFgcM50dUZUzOlPRmobNrU0Gg0Hg4YkkkZZYayjLBceTI+q6pCxLlss50+mEyWTCYrmg7QKjbte2LGdz5tMZWEcaJwx7A7Y3NtkcjsjTjCyJGQ0CJt/tWze4fm2bfq8gTWPyLGM0GjEchoCOLMtI04Qsz0+Rz5MkhPP3ej2GwwH9fp+iKDCmw5sO720glHzNy8Zog92XBzx69IgsTdnc3A73Sbnk+Pg4WIMekkiTRIpEgm06losls+mM2eSIdjbHNQ2xFPSiiMgalLfESEb9AoTj5Ys9nj17zv7RHpPjKbNpuYKUqqlqExRCERFrqKuKZdlSVx1YB7ZjNp8wmx9TlwucbXHS0C8GbA2GZHlKksToSEBnqRYLlrMpi9mCxbJicnTM/rOXlIs55bLEeIPD44yhWpTMJ1MiD10JmowiHTAajUh6KXEWk4+GXL9xgzs3bvDWzS1uvHGb3q1b6MEm/f6QYVJQ6IIYTZ5nDHZ2MCJCDAZkg35IUu/16fUKhltbJGmMtw3etnjhwNkv/4/6ust4dRhzZoisNZ6zfFhrX9+8T2Vcti4ujbugWE5mOT/nxfWddlyzPy5IHa91vTDmCqmng8brCznfxLoVeGFBl86/WEGt970wfnyh6rcsr0V037/+P/7VeH9/n6ODfYzpguuK8F7HnlgBQuKdoesaurbFeYuScgWJpNBxuuJWkug4odfrE8eatq15/NmvefzZr3ny+WOO9g9YzheUy5qqami6gDpuXUBOD6Hiq6fxNCWQEEqsC4gVOorpFb3T8O6bt29R5BlCCZwL0XdtW1OVJYvlYoWVV9E0geq9rirarkMiiGNNVS44Otzn+PiIxWKGMW1ARog00SoP5iT/SkmBFIHWpGlrIq0DrM5gSJIkp3lRSgXIpJP3UFme0e/3GQ4HwZWqJFGkGPR79HoFRZ6TpSl/8g//0WsdtfWv/89/NZ5MXrCY7IKUVKXBOgedwRPuBykFHos1NaZt8Xi8DAgkQkiSOA3I50KSxBlpkpFmMW1X8eHf/JJfPXzIi+M9Xrx8wWK2oCoDo3LTNjS2wXmoqkAZP5/XiCgiyzN0olbh/oGd+QSWqjcYkKYJN7Z22Bz1cRKcC+j9bV2zLCsWbcmsLWnbktbUWAymDTTzaRpjWktTlezv7zGbTTnaP8Z7F1I2Mk0kA8WIMQLnI3QsUB66puX5/hLjIooiYzjKSbOYSAsQDhFr9IpiBhWRRYqdYZ+tjT6JDq7pJE3I44Rev08cpWRpwp/+6et9n4zHD8ZwGmx3vo0x47XK0GV1vR5G92AcWq+QcWm+tS6nUXUXBj04F113roExD9aC5C6OAz4Iaz6RfdL1ZN3ji6PG8GD8YK0lHB+sdxw/uPr8krzV3FzVfrHnyVcYM37wAXxF0X2vhbvvvW+/7W1r8NYzm81IE80777zDnZu3mBwdUZVLsixFSY+1HVJK+sMeN27eDBn/KkHEfaK4oDfYZuvaLTZ3rtPZjvliyqeffsSvPvo5jz59yPx4Aq3BdgaBICsyZCTJipwsTwPwqxJkWYAcatoWYyxN3WCM58a127z37ff5zne+S5YX3HnzLaSS1HXDcjlnuVwyn89oqpK6rlaJvBVN0xBFEde2d8jynCzN6WUZu59/xu7uE44meyzLCd7bQHuvNGk8QKAxxlOVLdPZlKpaUtdlUERZSFAWMjAOWxcCP5I0Jolj+v0+g+EwYPc1IQzdE4I/pAThLUkcrC2Af/7P/9fX2o3z/ntveWdbbOuZHZckaczbb9/j/p3bHE2nzGbz4NrzXYArArZ2Nrh2/Y2QMK1j4mQAJIw2t9m6fv3/4+5NmmxJrju/nw8x3jHnN9RcBVQVyMIMUGyyzSDtpAU32slaZtI3CZlW0heQyUwmrbRtLbo1rNjFZrdINtFNkSDZQAEFoMb3crxjzOHuWnjcmzfz5asqEOhGAp4V78b12eN6+T+O+/+cQzyYEsYhVnb82Z//Je+//9f85MP3WM+WiKI/MxSKcRqiQ4UMYlQYgDOoMGSQRKRBSN0UmM5R1jUt8ODgIV95823efvOrDMcpL7/8KlopCtsyn12S5zn5Yk1ZrmhMxbqsqeqCxjZMp1OOh0cEBEjrmBzt8eFPfsLTJx9xtbik6xbehiSWYaJR0R5VLQnCkHJdcvb0lLoscaZAYzHhEB2lCCUQWnojsc6SOsAl7L/wGD2MEKKjWzUMEkW+rggjSdcZ4lQAfsvaOst/n93veZKRuWvBxN9svm5y3Pz84jXfVebZurNnd8DuKJT1N9lnZYId8ezu9j833O5LdkfS5ibLvth24Bfoym/Vdl++WnN6dsp6vWbQK8Au5gvOL865uLrk9OyMdbEmimPS4ZAoTXAIirKkriqks5i6oFxecf7kQ977+/+P/+ef/VP+9//tf+EH/+4vef2lF9kfTxAW8lXBfLZkuViTr0suz69YXC3IF2uK+ZpikVOsCvL5mtnlnKqoqcuaqqgp1iXL5ZKrywvmVzNWyyWX5xdcXnizRPPZnNlsxuzqivl8znK5ZJ2v+NF77/Hnf/7nvPsnf8KPfvweV7MrOtvSmpqPP/6I8/Mzb53CgFIBUZiSxAPCMEIHGq0VxrZUVU5dl0gpOTk+Zm9vnziKwHYIHIMkZjoZkcQh1rZcXZ2zWs4wXYPWiiSJmYxTBklMoLwyqdaeaBGG4a97GnxuWC/XPH1yQbFqGKYjQi1ZL664uLrgw49O+eTTM4q6YDQaMtobke6lFJ1htVxRFCWus1TlmvV6xqenH/ODH/wV//f/9X/yv/5P/zPf/9N/xde/+hbT4R6jwYT1omS9rljM1+SritPTGWdnM1aLFW1RUhclXVNQzlcsLi+oq5Y8byiKlqqoWFzOufj0jKuLOav1mk9Pz/nk03Oenj5hvjrnanXFVT9PZhdzVvMVP/7xj/nT//df80//j3/O9//qb/joo48IU0lTrpnPz1gsLmlKr4QbOEOqFdIFCAJMC1Eoqcraqz+YEiPglZcPeeHkiCSeAAm6UQzEgCSYcPhgDx3Bx+9/QL0q0K1FRiGtlZw8PsI1oBFUq5pIhEgr0UL/uqfB54eMm6tvdhcQXGfajd3Ntmtx4kbCjdvsFgbcri3bASRuLO7XrV+XynbSnkWCZ3p6u6W7I3frvFH/rX4924Hnt/JMevYZz+CXC/dixiVhgh1Ymn5bJYpCmrZlVRRM9veJBwlFkTPc32e1XjK7uuL8/Jz9gwNeffVVjuOU6WjIYDhBSM1ssSY3a8aR5eMf/x3N6orTjz4iny1xnaNpLVEQobTCNjXOdRTdilwsvbKmUqRpymRvRFk1tD1zrq5qFpdL6ODlF15Bac+ga43j8uqS9XpJWZacnT2hLEuEtHzy8cf89Kfv40yL1prvfvdbSO1ou5KfffgJ8+WMy6tLnOuYTMYANI1DSkGcRHRtR16sWa6XhHHEYDRACklZVZTlnLZtSZKENE2JQo21HScnRyxXS295IlKEgfSWD6RFCMtgECBERNca4jQlDAO67v6fNaRhiksEddEgREGSKmpruFgWHD8+oS4HVHXB+PCI2XrGap3zwUdnnBysefXVV0iHQ4aDCB0MCeOYxXJJlViaieQHf/V9ZhfnfPL+R5x9coZqIM8r4ihBCoXoOuy6pW7XVPNVv60qiOOU0XhEaUvqssVZg5GGxemMSAa8+MpbRENNmK9wLczzM1bVjOWi5OL0iqZtaZqc2fKKH773Q6xuSCLN/t4fkAwC1qs5q3zNYnbF/OqSxnQMwhiEpDYOF0WkScxQdiznc2arK0QccHjyCFN3nF3OaKqcdadIRp7mrgOFDRWHe4eY5gzlKmzZEERw/HCfclmTzxfsPZx66/+mpbUB+/sj6qb5dU+DLxaynZvdhTe7nemLgNR1vuyZerIbzW0/t03u1H6rod1any23my9j91gm6/+e0+0vEPl8oWwTn2W3xvSc/Nflsp3nlH2ONPaLhXshSXVdt7UarrXfftKBpjOGpm1Ih0NUELJcr5FKEyUpL77yKnv7B0RxQtu21Pma5eycMl+gXMPy6pTl5SmXp5/y5MOfc3l2RrleYxtvIseJ3uK4dbjOu1FwjXfJ0VYtxbpgfrlkfjFndj5jcbmgqzu6tmM+m3F+dkZZFJydnXF6espHH37Ez372M95//31+9KP3+Jsf/A3/9t/+O07Pzji/uKBsGpLhkCAOqdqGxXpJUax37ANKT1vesgkH1FXFbDajqmsmkzGPHj3k4cOHjMYj8rLAWcMwTYnDAKzBtLWnpytBqBXFesmTjz/i9MknFPmKUAvGwwHSOVbLBZPJmCRJ0DrwBnTveag7g0oj4mmKDANkGONUTGskVVWQ9mzFi/MZ1mqUTHn5pZfYGw+JtaQtCuq8oCgXlOUaRcfFJx8wO33KfH7F6Qc/Z3bxlGK5wNQdDg1IrHEIq7CEmE5AA9YI6txQ5hXLRc7qbEF+NWN1fkW5aKiLhtPTCz65vGS+LLi4vOT88oynnz7h00+e8rOf/Ywf/eTf8zfv/RU/eO8HvP+jn7I4v6KYdwSMvMNKZ1gUK9bViuVsiXXOm3MKFWXeYImQMsTVFecff8JiUZLGYx4eP+Lk5EVG02PWtcVJy95RglQGY2u6usA0DcWqI4pSTFMyf/pzlldPuPjwU0Lp2NufIE2LtRXpaMhkOsA5Sxjci/fazww35KZsG8m1SJPtZv4Cskpf3zMIku209myJ3aZ3F/1btV7n3BbIbqTeJjtktz6fEYeyG5F3Cz/Py5Pd6vcdWW+GbKfLu/U9t8AvHO7FjDs4PiKOY6y15PkarRXD0ZAwCL31hPXKU9HxZo/KxhvgPDo+YZCmgGOVr3FrGE8sQmpWywVFXrBczDk7PaeoGvJ1DkKihMBZQ2c7RNeBFDgne5vX3t18VdaUZYUQgs50WOuIkxiU5MMPP+TP/+zP+MpqzSuvv8HF5RX/8k//hA8//LD36hrQdg1tU9N2LUdH+7z40guMRyPWiyVVUZAkMVW+5tNPP6FrGsIw7CnrAVIIiqLEWovWwXY7TinV08lh/2CPYpUThSFd11CWDUpJJpMJTV0TaM1wOCSKQobDQU9HDxmPRjx+9IhkMKQoapwQdL11hfsejh4dEwYROMeqyFFaMhoPiXXIKl+zOjsnCEOs9rYVy6JBOMvjVx8xGk5w1jHPc8RqwWR6ACjyVU5b5ayXc65Oz1jmJVWvAqCkoDYdGItsvdsTZxWdxQOX7s8ia+9C3poOIQW6kahRwE8+/pD4L/8E8jc5efQa83zJH//Lf8Wnn36CcBAGgs55fa2u63jw8IQHDx4RqpjFYslqsfTM1CLn08tz6AzpMAQnCdIhQiqqqqHMa6LpmERCJAKCAGqTo1xOun+IKSsCIXCqoSxrTKeYJAG5qFFDzfjxAanTpJMhSTJAS8lgvMd0NCIMYoyrMZ2ls47G3n+nhxvJY3txGySu47YWH7JnhYWNxLItu6lrJwc37rcZ+7zZtq2dVm/mv9nx/vOO+C1wXZe7BqnbLd3sW5Zdj3XTq+s67xjKjXZug/DtfmfXn9lu+q0x/BLhXkhSRVF6hp0x3lNu76JjPp+zXq9pmob1et27oqhI04S6rrm6vOTTJ08wXcd6vaaqahaLJe+99x4///nP+fnPf875+Tl5UdD0TC8pBFIIXO9Pyjvt9UZDrbO951LvW0oIgVLKW7IINNZa6BmAH330ET99/32W8wUCwWQ0Jo0TrO08q8xYpFBgHY8ePuLk6JhABzRVje0MZV5wfnbB+ek5VVVtlYOLIvd299qW8WjE4dEBg8HA62/lOav1yptnktI7PYyj3n+V90uUJAnOOdI05WB/f2shfTQacXR0zNHREWEYUVZVb5pJEEYRSXz/LU6UuZ8nbdP2StmOuiyYL2YsZ0vqumG1XPk5U1aM0oSyqLk4O+P06VOcM5TLOUVRMV8u+dGPfsj77/+Mn7z3PhcXZ6xWa28bEYcSChzeQaR0dE5A12G7GiQIJ3HG+ecXKwaTkDgK0UrTNl6hu20rnnzyMe///CNmqwKJZDwYMYhSr2zuLEoohAtxneDRg0ccHxyidEiVV5iuI8/XzC4uWC2uWK1WtMbS2ZJ8vSJflQgD0+GIg4MDknhIVXRUVcF6PafpGo5ORoxHMUkU0bWSME6J04goGBBoSTKMGe1N0eMJKgpJBwmPXnzIi68+Ih4PqCW0nUVohU4ikiT9dU+DLxwybi+12XV8D2C3810Xvl7Qsx2E21nmd7JeA9Iu8eDZvDfLXcfczpPd/Jbt5slufNxoZ5tlp3zGjbTsutBz2r+7n8/0Lbudcnf8ryLoX32Vv3joOkNTdwxHIfv7+xhjKIqCui7oOv/mtjnY31j+LouaK65YzGC98q4OHpw8BGo++OADyrLsF2KHRmGt815ZpcQ5R++QFqUUovcnZXBo4XoL1d6VR9u2BIGXbtq2JQ0CBnGCtZaz0zP+5F/8C1557TV+5+2v8OILL3B1dcH5+Tl1XfXg1/oFomlxziGlpG1aZrMrPnj/faqyIAw9AC4WXpt/4wNqMExpuxaHQUhvTLZpvI3A5XLJ3nhKmsQEgSZJRsRxxHA4QAjBdDolCJR3bd+TI5xzFEWFdZbOWpyVBGFMmiRIef+3+5rWIIRhNA4ZpTHGGm+MtawwpgMnPBXfGrqmwXbGGwqeXbFaLZitl1hjONg7gXXJx598SGsaqqbGGEcUSCyCQGkEEISKpu2QzqECjegA6bDCEQy1l761Z0nWZYuQCmcM1lpCFTBOBzS148OPLpiv/jWvvfyYd95+my+/8hJnF+dczi4pqwrbdpi2IVQaawxSGrrOEkjD5fkZn37wM4q8YDQZUxUV5WpJEISM9vaIhgnxOKWWNaZrcIGjbTrqckXTWX7y/R9z8uCQo4dHDMYpIZpRqkmDhCgMSaMRwTjAdBAqb4eyKDvKDy+Q2nsvDhVEOiaOA6/8/BsRbksy2Q2wuV63dyJ27m6cr9yCuBs5e1DaSCp39eOze3nry7aO7HbqTnp2Iyl7Jmt2M20XQLIbN88Bqbva3/l+R3vXIJU95zn8w8O9oKC/9eabzhqLw/YLu6f+BoEmjEKMMQwGAw729zDGsFgsSNKUui5x1tJUJXEU88qrrxEEAX/91z8AqaiqmqbtaFqLMd5vFHiQcv3mnsaB8+1unoUQAnq6trN26+6iaRp0oAnDEIejaVvqqmHv4JDjoyOSNGE8GmGxVGVJZzzVVypJHAUEYUBR5FzNrljO59R1ySAOEQKGoyHHx8eMhkOGwxFRHNG1nmyRpAnOOs7Ozzg7PaNuah6ePAALbdMymUzZ25symYwZjoZopZhMJozHnoIexSGm7SjKgrIsieOYZDBkPl8yGI0ZpN4S+j/6/f/0XlOL33rzTdd1Fq1Nb4XEYq1AKwijAGdhMBywN5nQdoZ8vSJOElpTYhEU64I0jnnh5deRQvCjf/+3CBd45p90lHlN47w5LuFkb6Fe4gBtOqTpsFJAoDCmIwpjjHGY2gKOzjickjhZk4QBSsc0lcVpS1lUHOzvcfTgIUkYMhkNMJ2lbmo6a8FaVKhJQoVQgtlyRVuvyOdXLPKSo30N1qH1kP29ffb395ke7iFFiAnBUhImA9xSMTu/YDa/wJqWw+kRDkHnStLRhMOjB6RBwOH+HtLC+OCAZDxmPBqiEo0CqjKnXNfoICBOU7q6IA4HRHGEC0L+8R/8Z/d6nmTZhoKecUOWyjbp/Se7kkW2s4DfIc9k2Y7EtFM+u06/sy/cWs93Uq4lrTvq3PYl2025UfEu5v6Dw/M7+A9Mz7ZXlvErmSf3AqS++c1vOuccTdOwWi2x1myJFHEcYZ2hrEqmkymq9zl1cHDAT3/6Pk1V4Yy3ljAeTwDBxx9/glCq384TtF3v1l1r79XWWJwDgUD27uGF8Nt+1jocrnd5IbaSlxACa61XkA1DdKD91uNg6D3kFgU6DDnc30cFmrausUAcalSg0MovdpeX56zzNc5Yoigg1N5CxWg04qWXXmQ4HHinida78EiSeHted3l5yXLlzyneeecd2rpluVjy+PHj7fNK05Qg1EgpCIKA8dhbmIiiCPAWEurGMxaDMPZXL2X9/n/yvXu9+HzzW99ydC1d1zDLc0xnSOKYJA5JkhjrDFVTMx1N+xeDkMP9Q97/2fsUZYFtLVGsSEdTBI4Pf/4hgY4o646m7bxyddURxKF/aTGeqKCVZKAszgpKK/q5401gBYHEdX5bWGjpnXYGlrpsCKMYrSKKsiAdDpBakS9XxHHEwd4eYRRTFxVV2zFIQ1SgUA60FpwvLijXObrrEMKRJhYVpTiT8MIrLzIZp/0ugMS6jtHBCBVoXGmZrWfkzQrRSr7xjW/R5hVNXTA5eIhTkgcne0ShRsvIOxhNY/bHQ8ajEVL617c6925LGuuIgpAwSLDSIQLFH/ze/X6ZybJsu6hl7Czz2Rd5yc9ufT5/nb7O+WyZ59d+E+huIeXn1JHdZBx+Bkpd1/R5dX528KXvaCvbAGp2K2K3r79FIPXaa6+5NE0RQrBYLLDWL+BSSoQUDAYJdd3w8ksvUTcVg8EAHQScnZ6Sr9fk67VXXnSgeiAaDEfEcULbGq6u5hhjPUg5aDuDtd5aOcYgpNhu7z0jUcFWETcMQ8qyJAgC0jSlbhvvXr5nxknpLTnUVYV1jiiKMKZFKUlnvCUNKQVRFHpKuMCz8QLFeDxmMPAkkCiKGAwGPSXdUdd1T6JQhGHIeDzmlVdeZTqZgIOHDx5tnR1q7c9qlssFbdtycnLCwYG3zF2WJXmee/9XozFRnEJvpaLrDF9959v3evF55ZVX3Xg4wDnB1WKFMx1JGqAD7z45ib31hFdfeJGyrkjiBKU152dnFEXOarlkvaqoG8NwGCO0ZDQckSQDOtNxcT6jaf35C/h5YB2Y1qDaDlQA2ks0SnpHiEo5hBWAoHUtUnmL61VZMhoNiIIBq3xNZzpvYqizaClxSlLlFU4IJuMhVZkTxAFlVUHXQaCIkpDAgRYdQiukihkOBwyHY5xtGQwTRoMRg3CACqFsGlzdoAYBQSAZD6e8/uW3GKcJwsHLr79K09ZU65JkkFLlNesip+ssLzw6JJ0eMBwp2iJpL1cuAAAgAElEQVRnOasIQk0Yj5nujWlaA1LQdZbfffub93qeZBnusxbmu8AqYyO8bCSm65RbsHKz1POAItu9trVzYyXfjbqrk7ufd1Z8DZCb7ckbWLHb7DNdya5LZ9kO2NzR1F1N39UAN59t9tukzNu2hvl8SVFUJElCGIberYYx3pJCZxmPxlR1RdO0fPzxJ/z93/09T548oSgKtA5ojUUo3esu1aRpyng87m3XeVNAXdvQtQ1YgxQOrSRSyy2RwlrLLmhv7qMoQimFMca7DhGCsiyxnXdt710+ANbS1o0/65LQtbU3ltu0OANaKpRQmNbS1v4czvVgFkUhk8mEN954g3feeYcXXnjMeu3Pnqy1jCdjRuMxcRIhJFjnnR2OxyMWyxl1UzEaDzk6PmQ0GgKePHFycsIgHRHoiMl4ysH+EVoFOGC5WrFa5VRV40kh9zzYznJxuWCVVwyGCXEaYaw3BIyAurKMhhPqrqFta05PP+UnP/4hT08/pSjWRHFC0zWMpjFhoMkXJXE6JE2HKCWJg5AQgWgauqajKhukMCSJRqYhTgmk8MDknEMHGucUKAVSonWEcwqHI41ibNlR5WukgDDQiM6iAGehaR0yCtFaUK6XSAl5UeGc8E48w5gy78hLS91ohIhpS8nh4ZTjwzFf/vJrfOm1V3n8eJ+iXrBcz7CuZno8IU1Sr1oQOJqqoGg6osGYT5+csbjKSQdjjl98iYOjfZR1DNMBJyePScME0YbE4ZTxwTGDwz1kHLFYrlnNC8pliWvvP7vvGQr3dcJ18q34bLO63wCkbPv9dtwzQPNc6eh2L3bKPQcts9t5b7dza3y3JZ1nsm6/7kZk27hdgLvR3F3Adatbd3XvVx3uBUhtJJPNG30cJ8SJd50eJ/5tPx0MAMFqteJqNvPWvB00vWv2OE7wfAjB/v4+SmmEkL3ulUJJiVJeEknimKAHHQAn/HVX2EhWty/6y3Tt1j1HZzqMNTfz3Qhi56LfQnT+XGk8YTgY9pa8O0zn2Xx1XdK2DQ9Ojvj6136X73z7W7z55pdQ0nkX6qHfqpMCiqJgsZgTRREvvPAijx49RgrZS3ASIRTz+YLT01OePDkFK/yWp1Roff8PxIMwZJCmSOlfBuIoJo5TlNIEOiYIJKMkwjSGxWzFTz94ymKRY62gMZaqrRlPRhgDRin2Tw4JlUQIRxwPkFGAiCQylkSxZro3BqcpKtOTXiwW77bD6ywZEGCcg/7FQSmHbQzWOUB424LOepUH09E6S20MtqmhaxFYPHHQoCReutYK1zQEwnlDtIHDmYYkaIm1IolS7yQzjsjLjrJpvAqBdRweHfONb36db33z23zpy18mCDXTQUQa9R6qbY2j5slPPmCQDnj88mMePn6IDBQ69KBrrebibM77P/qAs9MzrJPEaYRQGiuCX/c0+PyQ7Qop2a207PnSScY27bbwk93Oe7v2XYkq25G9smy3mutS2W7MzbZuSjbZdZN937Nt1Kad2+PZqXan/d2+3+j37vjZvd0pmd0N7nd1/w4mxy8V7gVIlVXl98bDAOOcP6gOAsIoQgchUmtW6zVKBxwdn3B8fIwOQ6xzSKUQShElKcPRmMFgSDoY0DQtzkGSJL3fJefPoqwBrPevpPotPtkTJeQ1YN21CboLMcLh63Rme7nNxTURY7NlCFtc29bmLEghCIMQLRXT8dh74W1bVsslpm0JlGY6HjMZjRgNhhwfHvLKSy/y6isvY0zL2dkZWism0wlxHG0ZhIPBgCROaBrvbbgsK/K8IM8Lus4iEFuCyOb87b6Hsq4IQm9Q1eIwzqG0JogitPZOI2eLFUIGHD18xCtvvEI0TEEKhFBYK4jTAaPxmNFwRBrHVE2LCkKSOEUpQes6atOglEEJSzqIiKMAawVOCRwWKyxIcDisswhnsdYgnMB2Bh0oMCCMQwvPLDXWeDUH2wEdSjpwBrGhmUov0UvnSRTW+jYMFickprEESQQiYJgkHB1NoW1oi5y6rHFWkqZDppMhaTrg5OiAV155iVdfehFnGq7mM8Ig4ujBEUkSoeMA23aMJyPSOMKZmnK9Qpga03VcztZ0xlEsStqmpW4NQkj0b4DSN1zLP369zHYSsmdW2+yZL1lPUb8jwzYuu5mU3cy3Kb9pfxdQMq7r3taS7Xxs0jZldyKy7FZNz4wluwGM10CabbFj29WMaylqdzS749gZ7s2W7g7b8fy2gZS1lrquUSpASUVdN97lQhRhrEErxTr3+kNxnHBwcMDedEqSJAwGA4bDIcPBgOPjYw4PD4mimK4z2+250WjkCQW9rlPbNFhjvdVsIbane9uFG7Zxu5LTsxIV3mLFHVLTZy38G7CyzhIGIePRGOccSZKQJAnr9ZqLiwsODg546aUXefjwhLqpuLg8Y7ma+4P0NKZpavJ8zWAwYDQconVwo83NOVoYRoCjaWrCMOTk5ISXXnyZvb19ptPp9vnc99B2hqZrkDoiCALqqgEn+vEZpJSsqpqyq5nujdkbjRmlQ6LYz5PxaMhkNObB8QmHewfEUeIl8apGB5rxaMJoNCaOUqrKsjhfUi1KYiUJtffiq4MAgbe2jvAkAyElIECAVNIDjAAnBNYaEKKXrBwChxQOi0VJgbBedw9rkLZFOIewEoECJ7YvRSrQTKdjsIY4iUmCgGKVc/n0nOOTYx4/fsT+/gGuq1nML1ksZjjTEYUBTdeyWKxI45RpMsC10qsgCM9y1VEAVhBFCVYpTs8vONjTvHi0z5uvvcrhwQH7e1Om0wlpcv/16W5INHetrtn1Ir/JfiNLBhsw2S3Tr+rbPNlOWzfy7iReg+TutVn0sxsgsenq9nPbi9sD2O3jzf5nfdvX2LWTshnCjW7uDmS3pp1R3QLC54Vt6dvV/JLhXhAn/sl//d+6v/g3/4ayKNjb26Msc5yzpIMEYz39vG1a2l7rPwxDgiDwLte19udJxjAcjryV79rbs1utVjS9NYei8H6dyqJkvcqx1lPDnVB4AnFPluhBR/SPZUui6Psqdx6X/5/c4UTvin4X7nbwya9P1xFeggEhLPvjhIePHvD48SOGQ+/jCRxJGnN8coxWkrquUFowHo96qvmEvb09lAwwxhEEAU3T0DQtSZKyv3+AMQbTWaIoRmtvuePq6grXbwmlwyFxnGCcP/szxvClN96+1+LUf/VP/hv3F9//PlVRsDce0bQFbWsZTYYYY0jShLZuMaZBSX9WFPUKzlJI767dGMaDEUhBWTYMhil1U7CYLYjjlLyuKaqSMl9TX+Y4a7FaYrTuJScHxgOLaY13nyIlzrTIHoysE0gEGNDKYpzDBH6LUMlNP0BIjbAOKwVK+JceZzUCiRVed0/giCPNKJU8fHzMiy++yiBJMW2O7STj/YQkHLB3sk++LkhCRxANOT7eY7I3Zbp/gDAC6wRBoKnLkrZrGYxG7B8cUlcVDom0MSoMWedrVtUFdI7x5CGjZMx4L6WzhrZt6Zru3s+TDNxd22zX6dmOFPHZFW0W/JtZbwJHtnOzCzDX+TJuL/5sACrLtjE8P+ezHdl8bMbxvLFsK7xd8x1xn1HPXaU/L/xWESe+/Z1v8/rrrxMnCQ5/IG2Bqm4Af27TGUMQhERxgpDeBbtQ3j2BlMob3GwaTNehtNrSrqvKK7/WdQ3O9YqtG0+5crt/tyuBbEBkc3/nRU+WkP2b9KZMX8/22GpnnK6Hwl3prCxL1qs1Sipsf0b26NEjvvrVr/KlN97gS298iTff/DJvvfllHj96SBiElGVBkecEoWY4GmxNJsVxzHDoSRNSyt5zr6/Tg3rcu12XNHWNsR6gbhNG7mv47u99lzdef504TlASlNS0QFE1OPA6S9aiZUAYRoRa9i5KHHES+t9JCsqmpmkaZKDYG0/pKstyVVJUOZ1tAEugFForOiewUuKkwGqLDTpE6Lz+nJQEoaYxnX/ZsQ6JQCPRTqGMQHQCbRQgcQqMMjgpPUAJhen18SwCawVWgMFgMSAsRljKuqFqG/JlgRKQlxVWaY4evsBb3/gGX/v2t3lw9JjfefsrvPqlN3nx5cdoHbKaL1nO5gRpxGB/gAo0UZqQDgaE0jMaCS1O9gaNA0kcB2g5IFBx/wwc67KjbDpaa/xW530PtwEq27lnZ2Hfihy7YHJLPsmy5xAtuJX3VhubWnckpVsizPa86Jnqbwxlp5/ZpsVsKxFlmwHdbGCnzd0xPTuOm926O89n9fFGvdlz4n/JcC9A6uD4hG//3nd59fXXKeoagz8k76x/K207b2UijBNG4zGD0Rgdel9PRVl6p3R1Q1GUdMbSdh3L1XK73bVerynWOVXpTc1IKdB6s2UjtsCyC/u7S/ZdrwObEltAE25byu38bTJvzrm85HUNWHVds16tWC2XBFr33ncfcbC/j8Szwg7299mf7jEejtibTjjY2yPQmuV8wXy+8Fuaw9EWoGazud8+TGPatqGuvWX5/f19Dg6PGI9HxHG8tbgRBAFxFP/qftD/QGE03uM73/kOX3rjdaqmpXWKOB3QdBZjBW1nvJLtYMBoNCEZjNFhRFk05Ouyt7LfkJcVTWfouo757AqtE5LRiIuzK+anVywvVjSVZwxGiUZpiTH+FwsCAaYlCBQqUFRlTRBoP1eF8md8/bwKIg1C+nNTVH/+2RMthMIJhwqktyYCoAROOC+uS4cRILTEKYPp1jT1ivVqRRLHHO4f8NrrL7KX7iOUIBkl6DBib3rAME7Zm4452D8gkCH5fEGRr0mTmCRISIIUZyzz5ZwwlARKsJgvKWvPmjzc2+d4b5/D0QDlHJEKkEaRRBGD34B5AjtrOjvCR7a7LPfXFtA2uXfL7aRfJ9/Mt/lnp8rd9BvS2o282U7cZ4TdzsONvu6O8dnKshsx25RbktvzZaTrks/rYrabequa55X5h4R7AVKlsXzla9/krd/9GlXb0naWIIxRUnlWVN0QBgG2bamKkkBojvaPsK1jcTWnqqp+u6sB52jbltPTM4QQHBwcEMcxUkmMMbS9mSWH895NnUXubOndPke6TS7YkCyQ0r8RbyDMSU8ffoYFyPXVh43U4okV0LYNZ+enSCU4ONgjDBWL+RXr1YyuLbFd5R3lrZZEgWY6GrFeLjh9+pT51RVKSaI4QghYLpdcXl70BArBOl+xXMxo24Yo1AxT714+jgKquvRekPt+3PdglOZ3vv5NvvyVd8ibFuscSRyjlT/TsW1DGIQ401HVJaFQHE73scYxv5xR5d4PVNNUKOmwtuHDjz5Ga3j84IBhmqBx0NSYtsE4iwoA54iURDWCdmXBKc8QdCDDgM5YwiSEQNIJiUHQ0GE0dFrSSIUVEiE0ogs8Td2Z3r6fwVhBTwIEpLeG4gSBVGihiEKBcI5VWfDhx59ghWV8MGK0HzO7OKc0Sxw1YeCYXzyhrguiUDEeDlgtF5w+OePykysCpQiiGCEVRdtyeb6gKzSxjnCqIS/mOGsYKdgfDRgOYkZpRNfW3iq8kfwGaCrsAEa2/bq5ybYR2U72ayDaSC7ZrXT/36362AGh3bqvo+7uXJ9xV8j6rDLZTqYeN7d574KQG/17poHb+bM763g+Ku98zb5g2i8Z7oX7+L98/9NMJQPS8R5lVXM1n9M03qq3sBaFZ8C1VY1tDXEQEwUxbWtwDrQKiMIQ03nX6XvTaU8k0BhjmE6nDEcjdKC9VIPzYCWEt0phLaLf/++NISEQPZj023fbS/jzJ+nt4lnrtpTA6z+5vdwOQN0goDsHGHAt1nUgDEdH+4ShwnQ1UhiUgjJfUuYr70VXKwIpkc6RbNzFW3/w75zd9j1NEk+x7zrSJEZKSRKHaC1Zr1d0bU1VldRNi1YaJSWm69jbP/zv/mP/9r9I+Isff5LpZEA6mrIqSmbzBU1VeXKcgaDXQyvrGtNaYpUQqxjbGaQQBFoT6ADhLEppxqM9JnsT70+rMQzSKZPphOF0SLloULGiaVuEkpimRgpveNZah7QgjcGrjktE159XOocSXU+WEN6eHx1SOOgs0iikP7ECRG9g1rtq8UKUI7DGn0t13u6/aVra0hMwglBwdDgm1AGLyxXDgaItGvKrOdVigZOOQGsiKbe2+KI4wDn/m3fGILC01qKlYjCKwToSHVJWjr29EdY5Lpdrys5SVTVtVyEcBAq6pmbv4Phez5N3s+9lZO9C9j16/+w8z3s77CR/792t2/V3eZd3+5Qsy3j33Wt37e/2Zd7dVJll8O73rp2sv5tt2732zL4pcd3qu70/+CzL+B7vbr3XZztV+L5kfYmMd9/NuNnMuz6ed/t+vPucYW5i/Ziel2ubJ/se2bu38u0OHO58ppvkd999l+9973u/knlyLyQpKxWXixU6jvnq17/BCy+/gnGwLop++8RbANBag3Ms5nNOn55iO+OdFyrpfVBpTdd1LJdLqqrasgaLokBKyd7eXs/+i7bKu9uwYettQm8SafcMqk/AOrDbDbtrvadfKGwEsJ5yL4TcmmCq64rlcsFsdkXRk0iCQOOspSoryrL0BnJ7U05N21DXFW2/lSmloGlrjO0oq4KyyrHW27tbLOYsFjOapqapKuqqpKnrnrBxv4NViqvVmnCQ8LVvfYsHj1+ic4K2q1HC6w8IyVZ3bLlccfb0HNdaIh2jgwAdBigdUFUNRbGizAussZRFQetKlNKk0YBXXn1MFERY4+ga/1yN8bpRQgis6YkOwks7SnhJWtBbqRAaKzxVXkhw1iFQSCdR7Ejmxr8QWetJGc4anBO0rcNYr+gehhotJW3ZYq0jDBUqkKgALi4vWCxmWFtgXUs6jJDS0jQNy3WObRu0MCglyeuapq2om5YgEESxpixzrGtZFmuKZo0xLcIZFlfnVKsrqAq6MqeqcrquxfEbIEplXEsrcOdr/e7L+U7265jsOmV3Wy3jut6dLL0U1qdtCmS7UspOH7Lb33drfbYvu3+7+W+McTf+dns3vmfPpt16Ptl2zLvt3FHt7Sbujvqlw70AqdYYrHDEScLRgxO+/d3vMj04wAJN19F2HXXToEPtDYliqMqctq0BS9C71QCoqorFYkFd197OXhCwXq9Zr9fkeY4QYmvjbmMG6Tpcm0PabP1tqeS3ceg2K+I54bOIFxtgkj0gBkFIGIYejPs2VK/QnAxShJKUVcl8NmM2uwLoXXNYyrLwLEClGI9HDAZeibntOq/DI8E6x3q1pigKrLU0dUVVFlRlTlOVv8xP+B8ldNZisIRhwuGDh3z3D/9RP08klelojJ8nKpCkSYgzNVW18lbObYdWAqk1QnofYZcXV7SmpbMGrQPvqdfkLFYrrJSESUqcDvyLhBBILRFK+t8lUohAeYsXxtJ1ZvvC4YTCT5j+FNIb+8M5T/vujDfLRQ9gwjm0ECgpQCmMlMhAogOJDgHhiNLQk4KUwliJa2GcRgRIhGlxFsb7UyIdgHWsqpqL2ZyiWFPX/jx3kChsV9CZmiDU7E9GTNIY0Xg9PynBdC1CQlmWGNfSWMsyLzF0rIqcoih+nVPgC4VnASi7leGZm2e+7a7bGTeX+BvbiDt1XYPUbvnrktu+ZNd5bzaeXbedbcBiZwQ74LRN2wLhzbE87/vuODbEjdv1Z7dz36gq+1yggpu/wS8b7gVIWSmQgeZiMcdpzeTgkP/8j/6I73z393BS0TlLUZXkxZq8WtN0NWGiiJMAazvKstxSzTd6P1EUUdf11oJ613nLFHVdA971RxR5ptuuNLQFrd7v1G1w2QRPznPPB6EvdHkPwZ1xdP3WpVKaMIyIopg0TQnDiLZtWS5X5Oucpmkw1iKklx7DQCNwFHnOYj5jvVzQtQ2ma1gtFwhriQIN1tBUJc4ZkjhiMEh7AkXkCSTu/r8hWyFBKc5Xc4JIM5mO+S/+yz/ia9/+Lp2SVFjWdUmxWLJYzalcjRxIwlFI6xryqqLIS0QYMdzfZ7g3IdAReVHihGNvf0pRVhjVUVZr4lQTKcVoNMB19C8lDqnwSrnSgRboSKAiiQokrmdpCtOB6Xp9ON9/IYX3RaUkQoGTeCVy4e04ekUr6RV7lUMoi1KCvDLkbUMrLJ1pPI3dWJTTBDoiSVPS6ZSu61jPryjWa9quQUiLDhVCB5jW0RSW+azi6mLJbLGArsGtcyjOUVoSRhF1a1nM5zjnySIgGKUBGnrjs/d/njwjqfRft9EZd4BEdqPYbumMDXBsCt9uJtvBj+xG6ezG/Q5U7YLPTt7dPm7QLnum7DVIPYMFt78/J3lT36a93VZ2c9xZw3PauFH6c/rxi4R7AVIqCKjaDhUElE2DjiMme3u8/c7vcvzwBLTE4Gi6lqIsyMscYzr/5mc7yrJgvV5vwcr21Oq6rsnzHPCgNBgMtlLKhtWmlOy3yK5JE1vlXXbo4sDzadr/kO0+D4x+m4eeZm9pmo6yqntHjS1t05AXBVVZ+7MQKVFKg/Aeda2xDNKU6WTMZDwiSWJwlq5taeuGQCu0Uj2LcEnbeIsCdVUShgFaiX7huf+Lj44DGmtQYcS6rAnTAePhhHe++g7Hj44RgfM0ceeo64ayrsBCAB7I1zmmK6mKFXWxxPUmjrCddyhYtkQqZDIcE+iAtumIRwm2k70Sr0BJEE54o7LGISwYQ29r0pNVBM7bN5K9Qq6QGCMRUiGcV13wJ5Z+7gkpcEjo/Zh5ZBNIFMY6pNIoqQjjBGsERQlNK1hXDeuioC4ailVJXhb+hadp0BgC6K1ZNDjRoXTCYDBiME5J0xAjBEIanG1RYYBEUxbeceiqrGjyhjCE4TBBBwLhOoT8AtsHv+ZwG4CyPjK7TuT2Snu9RffsApttQGgDUtxeznclkuv0TcwzKdnu9+vuZNu/bbaduOx6XFtEzHZA7Lr/NxkZd412p4FdUMqehc3PDTt1ZDciv0DZLxjuBUh1OOI0xQqBcQ4dhaAVUTrgS2+9xWg6RUcRUiukUjhnabqWsixo24Yg9K4Vqsqf1xRFgRBi6+JivV4D11tvWuutx1qvTyR7Zdxbtvo2dInPsCjhr8/Y1vvcS3mCBZK27ajqhqKsyIuSqm7ojAEHYRR6HSflt/CWyyXr9Zq6rpBSkKYJw9GQJIm3Z1U6UIRRCML13lrXPaCvmF1dIISDnnCh5P2n93U4oiTF4midRSchnZSE6YAvv/UWk70pYZyA7AFBOIzzLzFd1xIGIV3T0dQ1dVtTFiWudShCnBCsizUGsE5iLARKk0YpaRqjQ7+FJ4XGGQ96tjP9y4x/fkpeW4fw7YvemgQESiKt284Vb+yvN40FvRQtsZ1X+FVC4qxACumnoBGEgcZYiRSW1rUUbUlja2QoMLb2xB8dMhwPAe8gdDZfMZst6ExDrBWTUcrB8R7pMMbgKJyiVEMCleKArsmpi5y2zVmuFsyvLrxFCqFw1vEbgFHATXnA32Tb79luvuxWvp2468X3GjyyPno3y7NSw3Vbm3+y3bTspiSze93aK/S5MrbbZzf6n12D17atHeB6Bqh3x3ZjADfB9Fbndwpkt4vdrmwn6624XyLcC4sT/+M/+2M3GqRcnJ0hbYdtWwIF87OnSNvxo7/7O37yw7+nWi+QOLqmJtC6P8vRTKf7GOOJEBtX7EdHR8RxTFVVLJdL0jTFOUfXdVtPtYvFgtPTc2xvK22j2Ap4xpaQWzLF9XO6JlNcSx/Pf4bPt4nnerM6Lc5ZBsOU73zr6zx4cEQUapR0TCZD0iRmkCZMp2ME0FQ91R5JMkiJ4ogojvotwgitA6RU/VakJIoi8jyn6zqKvOTqasZoNEKpgIcPH2AMW99Tb3/tD+81Uv0P//yP3XQ44PzpE085r1uiSDJ7+hRFxw//9m9574d/T7VY9jbwGoTyLzax1gyS0ZbRWdUltrU8OHlIEITUxkua6XBI13pdO+EgCkPKtuCjj5726gIdbWNQeH9fzuIXcNe/qEj/EmWNP2NyOBBe8dc4b//OTxeLBdTmXNRJnLWAd/cijKOT4LDUTUegWnQgSZKY77zzO+wf7nNwuI+mYe9kD2EVwzRmPJ2iMDRtg+lacH6eKB2wN5oShwEi1HShIJQaZ8E5TRSllMUKYRqW8zlXqzWHJwd0a8cLLz2ksRqhHIFQ936ekGUu29zeXny/SPEb+bOdyIzbC/KzMXdWdrMvW1TZxZWsB7xdSNxpfwfWbrd3A6S+QMi4rm23X59f4rNre+bZ/DZZnBBKMF8tiQcD0BqnFbWxTI5OiIcT3v7q1/juH/4h8WhKYz3LqxMglKIzhouLC9brNW3bei+6zjGfz1ksvE8lb7cv2EpPTeMVXIMgIEkSgiDwFOCewLBh9W2sNcC1pCS3APXFwp16U75GcAJrJc55/ZP5cs18saJuOqQOEULSti15UZDnJWVZ44B0MODw6JDJZMxgOPAmekzH7OqS06efcnb6hOVizmo55+OPPuL87IyyKIiigJOTY6aTMXvTERcXZ1xePOHq8pzTp5/8Sn/T/xBBCMHV1YJ0NEGGASoJKKqW6fEJUTzi7d/5Kr//j/8x8XBM4xwm0NTOEmrP8ryczVksc5quQ6oAowRni0tW5ZKu9SalhBXEQcQgjbDK0mAQQjFIEhSCQEm0cijv6AkZa2/oWAK97h04lAInHa4nRgjr/U456/NsyDn+XcUihUNogdWO1rVYYRF4MkMUKHQY0hqLU46lLcltR1HVGOFVDRAdjWtoyiWmrcBBkgx58MJDJuMxSZrQ0FI0BedPTrn84GMWZ09YnF2wnM35+IOfcnF+yWpZEAYxJ/vHpGrI/sGU2eUZi/NPuDo758mnH/9a58AXCtnu7e1lPeP2wn9X8c2Z0TYmu1lx9pzPG3HbKrKdvjxbz4bAsAWqG9LTrvRyG06u89/uwGcBT7aT57kCz62E59f2vByfX+KLhnsBUrYnAljb+4QKQmQQ0TmBDELCZMDDxy/xxltvk4zG1J0FpTEIEJK2M1uFXt0TAH4AACAASURBVNebPtpITRuw6XqrFRsHhV3Xeft9PSBttaB2qOebcJPl94u9HNx22+FuSF3+LMJT7CVFUZEXFU3bbc0qdcbSNh3G+IVNKY3WQS/5dTjrlUJx/nvXtaxWK2azK09hz9dbHSElpWe49Y4Xw0D39H3RMyXveXAO0fvjEirACY2OEzoHQZqQjic8OH7M21/9XZLBmKLqCJKQFkknNE3rqMuKumk8rT/0um5N0yKcoGst1nol3a7zdhzbqvHt9RRzb5vP6xkZ6anoW723/mxRKenZe/108Xb36fXw/HwynUGK3kW9FBgLnXWgDGjPAtwo2UmpcK1nABprWeQ5ZVPSNH6e1JWh6yxN6Vl+1iqEirEioK07irJBR4rSVFgFRlmsaZmfX1Hma64u///23rRXluS88/tFRGbWds7demGv7GaTEiXR4iY2SVFbi+J4MCKp0WJjBpZmSIqUZA3l4VgGBMh+4QQMA5bh7QvMCwPj73HhD+EXxsAzkpq93HvPUksusftFRFZl1Tm3m2Q3ydNC/bvrVC6REZHn5I1/PhHP83+W2OBRZWAyk8xmJVF41KxicecEZEEMnkIGYrz5oQqHU1r7nmb17sTe1Nb+9TtjZ6CW/Bm+6v0Lt9fUo9q3TY3arOsDAryuCyPSGvZGXdi7qt5rKJWux1bS1fvbde5xJ8fnd/05KPD466+5v/eCG0FSMe6044SQIFRadFYFsppSzU8Q1YSP/MzH+ejHf4HTO0/go8IjcRFkJp5BKHWc9t37pI5trU3ySHm9anCsGGvXDR57e67oP2YpBikVQiicC8laavtEoDFHpAhBzFNCznmMMdu1N6377RpTn13Iy7IAAn3fYY2hKCWTaUVZFUBEmxQTFWPyHCMGikJSVcWP9T7fD4QQ8vRZXtORJVGmeKQoSqKaUJ2e8tIrH+NnfuET3L73NNYrDCUuqq1ylbceZ0NKpxEEMYo8DayIytG5BhN0WgtyDqs1IQYQMVtCIk0jppk8ipQJBqUUSgmidyiVLKUQAqpUWQopkVUIKcUIUSCiRMScpVmlbRFECvCVKVV98AFVVclLzwe07el1j+07ZEzegM56nI/oAManv7O1HZtNg5CRi7fOaNZrtOsRlSIWybO07TuQhs2mI1qBoETbgHWBvm1wJgUzKyWYlJKy+ACk6qgPv8eDfr1fcMwqu+Kj0/WIkEbl6wPLaNvGASnma/fq3NvYJ6T9/hzWs/ve3smojsNr9suM+rvtKD8y6v2732/5PdR7HW6E4sT//f/8v7XI75ppXj8LtsqU8TTEZHFU1ZRnn3ueZ59/AW08b7/9gOjTYm4MOzXvwUoatgcHicEF3VpL13VYa3NQ7/UJCoW4LlB32P/h1/K2aUC2xCeQKq0hBR/w3lJVJSeLOdPJlCJLOfksXdS0Od6rbem7jvVmRdtutv1xzqK1xhhLCIHpbJqCfU1KXY9IJJbc7gPLyzMePnywbfP5l37hRisJpOckK3zImL3lQMkUWxTLAusDk3LKs889x3PPPY9zge+/fUaMIENABovz2aPSOSApjrgQmC5mBCJd02OMxllH17Q4Y/HWpiDekLPrmqQWIUO2wYsCFyNCAUhizCRESIG8hSDEtDa1e4TS9LHPivzBe0SQqFgQQyQgkSIiY0QWFcQS10MpLFVUTKo5ZVlRTiRegHWOKAJdv6FrN/Ttkr5taduOvu8RPllwzlis0xifHJBUgIJAiB6U2K49VZXAes/l2ds8evA2i5Mp09nNf07u1/fT+PkacD+rR9RZMeK116jvZzWJmlRokHo4wGv3ob7PVvlhpDGxJZbX7t/PqgyvZfUHqO/fTyTw2mtJtaGut0oWde5LfR9Ssfu89tr9rHhRU9+vuf/a/cxh93dNv0Y6x/1EnK/d5/5r+Rz3uT+Sfxhu5fCWXsvyF/eH27g/KlTvttPm6EC+161CxnAfj20vbf2DUpyI3hNzMIkUMrncFiWqqIhC4aJkdnIHigovSp578RVe/eKvcHrnSVwAnd3Oh/WkEALW2qTVZ/MAzVXLaHDnHtQqlNoFBb87Bb1fFpbYqrg7FzDG0vWGrtfoTC7WJvLp+56+73HWIaRgMpkwnaZU6NZo2mbDanWJ0T3EFKxrrSF4h7MG3Xd0bUPfpUHr8uKCB2+/weXFGcHf/GmcMAjmxZifE0VRFqiiQlYTjI9Mb90BVUEx5ZkXPsIvvfor3L13Dxcs2uks4JrCkZxzOGOwxuC8Q1u3ta5ESO9JQUSiEhRVSr6pqgqKglJIpEt5o0SRnCGkIClIeLapXkRK2ZtUJrLU1hASFQlJbUSBjz7HUGVdyUIhREAQEIXE20ipFNVEET1Y41n3hs5YjLF4nWLjdKfptaNpepxLLyVFASd35lSTCh8szcUlXZsUJLzxGJ9JOBiMaenbBqMb1uuG9bqlX605e/sNHj14lJOG3mzUw496fLDefm/tjjrvX7Eq6tHPw70Di2Fcb72zaOrtfn1wAVsLba9bY2vsMRbK9ujBve0sxHp3/kpvDyy26+5l/xd28Ourrzkz7st+ve8nbgRJheCTZ1Oen1dSpQSIqkTICmRJNVsg1ARVTiknC1546RU+/Uuf54mnnkGqYuuVJ3Oa+MGq8t7TNA1N02zXrJRSFEWxJbU0TZNTdwz4CXk9DvqAQiZhUWNddkFP2XS1Nlui9T5NS0olmc5nnJwsmFQVWves1yu0Tjp2i8Wck5NF9j7zQ+QxwXuM1qw3K87PHrG8vODy4oLl5QVts3m3rv7UkSSD/PY5KYRCqhKhKrxTRBSzW7cQcoqQFdP5LT788kf5xU9+jqeeegZVFfjgcyxTCkUwNslFWWPou4a+a3HO4F1ahyrKEqRATcocZJs+IXvzRSI+JE9NldUoBAIlyFqKoKTEGY9ApLJD8HhOy4EUqT2VrKpAIMSAFEklJCLzWqvKkwslvQfj07SuzqEL1hhizvvkrUOlXDIp31YEEzUXDy/ouh6pBJOy4u69W8xvz4hK4CMIkkXf9h3rZkW7Ome1PGe5WnJ2dkbXNT/FJ+AHQ7398bgCNeMBvR4NtsOZVEW9nb4b11mPP1siGpNffbX5+pA76qvHt4fGvRjfUD30altqqKo+7OB1FLdta/fzgIr2+n7Yr6u79RXnkMMa3w/cCJIii7yS3aaFlEhZIFVBUU0pJzOWm45yMmcyPyUIxfTkNp/93Bf4/Bd/mVdeeYWiKLDWbslq7CwxyCIZY7bHd9p9V9eftsTxY16PSm1BSuSalC+MsTRNw2bT0LQdbZuIyjmHdz5PTaaBL3hP2zYsl0s2mw1SSk5OTrh79w4nJynPVIwRa83evW/Wa95++y3aZkMMHp2tqhsP7yEk60KKZA4pNaEoJ5Sq5Pad21w8WlNM58xPb+MDLG7f4fOvfoFXX/0Sr7zyMWRR4Y1FiIgUZFkjT4iBZrVidXlJ8GmwD96jRFofis5vp3+jyLmhZFo3lCJ57TnrQSqiYPscBtLgj0prQCq7oIsYk6UkJSGkeLlgQgr0lakdFwQxKAggC+h6A5Ss146zy5bNZon1KUVN23ToTuOsx/SWCAQPpxVI29FsGpr1BV3TU5SK6bRkcXLKyZ1F0hYkJouy03hj6XtL17Q8fPA2F5crTPC0puXRw7d+Wn/9Hx71wU49Ol6Px9b6oEgqsB38a8ZMsFd9Lrm7eFSuHhfcs2RG9YwLDdVs6xm1c9A2jLtTP8YarEfb44vG/Tu4btzmNccOC17t1V4T7wtuBElFP3acECmYUeWpOFUwmcy4desOqBJZTKhmJ2gbeerZF/jt3/l9vvEnf86Hnn8Z7QXrzmBcevuElPgPUtK/xWKBlJKzi3PavssabtniGpwnyLQlkq7aOE/UVbx3EosxvbkODhx9r1mvN6zXiaRSUK9NThPO0featmnZrDecnZ1xdnZGjJGqqpKrfVGwWi15443vc3l5kQV2O87Pznj97/6O//j//QfeeuP7LC/OkVLwxL17FEry1ptvvOd7+XEj+izCGgEUUqikFjKstYmSe/eeJIgCygo1mdObyFPPvMA//ae/x7e/9R2efuEVGqdYtgYdBT4KolTJiu0s5WTK7OSEGAWPHp2xaTZIJfCDc0+ergsKnAxQpIDoQoKSEUkAEYh52k5kpSMhAlG4rFZPmksU5Fi5kH1BJDEfL3AU0qOKFI8XgkMUAR88IYBpWrpuw8OzM86Xa1ZNR2csfavxzuC8YWM6zlvDg7MGvW7QbWQyLbjzxG2ErWiahtf//nUuzpa43tB1PZfLFW+98RaPvv86r//7v+PBozNkVfLEvSeZqJKHbz/46T0APyzq9GM7mNf7A/MVm2H4Gs6NLtyfUts1MK5ve/Go1hq2BFLvnd/VX2/7WO/8Mq4jpdG9XLVXru/fdqselaoPzu4RXL0rd+3+iJQPm9/18h8eSfmYLKkYQtI7CwERIzJH8kuRLAcpFVIVCFWCKglqQijn3HvuFb70W1/lIz/3aUR1CxNLrE8OFyAJPmCNAWA6m1BNK6IAE3zKspH4jCBIKeFFSgnvScK3u2OjTm8vfGeIUYKO7QxiYsHsopxcxCJhm++q7y3rTcvlcsOm1WjjaNseZxxKKgol0F1H3/UYY4kRiqKibTuWqzVEmFZT5tM53jqs6bl76xZ3b98mOMut01OevHePl198kQ89/TTz2Yxbp6fv55/0xwIf/G7KM9gUX0SaFisnZfq9ikhRVYQoKRdzrANLRSgWPPH8R/mV3/oqP/OffBY5vUXnZVZwyFmMQ8Do5NV3594p5bTC+YD2PrvlZWfyHIwrVCIQKQQ2OFARRHqmEClHFCE9g0JGlIrJo1KKnNAzpuc8krK2IPEerEnSTj5IfBQEkUItvA84bZAqOY5Y62g2DQ5P03dYn9Yzg0vpQ6L3dF1Pv7F0aweFYH5rTrPesN6sUTJwMp9xMp1gu6ReMZ8n6SSjNad3Frz04jO8+MyH+NCHnk6qJic3/znZWSN5tx4dZ2f9jImoHhesr46xY6sl7e9K1LvDex2oc7kRlXFw1a7uev9oPe7s0N961+6WKOpd5+rd0cPe73HQFZKiHpd4h+uH+q9pqd4r/n5y1A0hqaz4MEy/iRgzMUEhU+R+CEnRXBYFSIWqpqAm6FDgijmv/tpv8fX/7A/51KtfwsaCyewEKSv63lCVE8qqom0bmmbDfD5HFUWSHBJZ+DN7NeekPikQM5NHmtQ5kEVC7BHQYz9XpgzFtqb0Rh0R+RNicv3tes1ytUmL1trSa0vbpsyywaf1O2d01u5LU4ZSSpzzOB+YlBNOF6dUZYXue6qiYLFYcPvWLU4WcyqluHVywr1791jM58ymU+7dvftj+uu+f/AxAoHoLRCJPhGGlJEipygJ3lNOSoQqiEJSzKbIyYyNV7jJHX7pi7/JV3/nn/Gpz/4yxpdMZ6eIWNJuNJPZnOnpLP3uNw2L0zlqXmCi3TpcCJlc4KMMOGlxpU/CrzLiZXqpQckkISSTi3kMEh8U3kmiE7DNPZV80pMLevLkKyQUpYCkaUHwgyXvE9FVEhdcyhCgLc2m58H3L9Da0vfJ8cZYizUurd8Fh7aWECEikqOEsahKoqKiqk6R1QwdHVVRUMqC6WTGnWfvcef2KW5tefrpZ5iqitPFgifu3fznZGe1bHf3ZupqDk6Oj9W7UtuBux4P4jVbZ4v9mka111vrZEdm9VDVruZ6RDzbTh7UtmWUent+V6Le+1xnwewR9MG53Yl90h11dFT/rizbXlxb4QHlvXfcCJIyxmJtGnClKHJyupSgriwrqiJ/qglVNaXMH1VVUFZQznnupY/xpV//Mt/7y7/if/gf/4b5yW1a7fBR0HQdy+UKYy1dr7lcXmZyyErmMacozOkShimkFGKbCCRhR1SjaKp3vb9DntrmEiI5jaSUDfltOQS63rBeb+jyGkMiqTRt17QdXasxxmG0zSnTIzGAkiVVUeG8p885qZqm4aknn0IIcM6yODnhrbffJBBYrVas12v6vt9T17ipMDYNwMZJAhWTWcWkKiiloionTMspRSyYlgXzxQKhKor5AjUpiaoiFDM+/JGf5Td+87f4N//1X/E3/9P/ysnJHdatIwSJNT0XDy8IwrFpGi7Wl6iJSOndc5JAEQUyq98jI6oEKQNKhO3LRgxJ/Jj80iVjRAWJzCnkhRQpJipPSUcREUogYvIWHNT1CyEopARUViSPlKVMzg1A22ms1zRNhwTM2rFetmmqWGu09nTG09mOXnRJHd8LUAWFLAlS0vc964slfdvxxN0nmZ3OEUWkQPHmo7eYnJRsmhWd6dg0bVK3+ICh5nCA3lkEjLa2hYcy15BHvR3t83X1dSS1Pb0/zXZwmlHV9XhnVNXOamFHHOPO1rt+1bk/BxXs9rb3Mu7LqMy2b3X2ThwdH93PjjjHd7LX6rXHf1TcGJIaYptSIK6iyAQ1uIcXRYEq1J67+BAI62Nk3Tb4GDi9fYdPfPKTfP13f48Pv/zyVs1BySKRYdbuM8ak+CMfCT7kN9r0C5HJWSsT2NDLMPoe/IvDDyQiu43/OmArwc6JMC1JJE9DqZITRUovkiSctDVoa9HG0htNbw29MVhn0zRQzmfkfWCzari4SLJQZVlSVdVWXmm9XtO0zTY55Gaz4fz8nNdff/3H+jd+P2C0wdmsryiAKKiqkmoyyd56iul8QlFICqWSvqOUFKVClQoXAhvTYmPg1t07/PwvfoKv/8Ef8MJLLyFLhfFQViVWa3x0FLJEbyzCRUIQOBcQUUCMFEJS+hLRSwSRIgpkAELcxlOlt6Cs8ShissIKsoUOCJnyScld3FfMqulAaitEbMhT1zEio0eoIgVfe48g4qKi15LNZYvudQrGtQHTWtqux1hDdAaZE2TGCNY4urbl8uwB/eYCKLlzZ060KTHi5cUFFw/PUYViuVyzXjdcnJ/z+us3Xz4LOBzrt1v77/j1la3x2asn6v3BnWHMv8Z2qMfnR9ce9ufwsmt6syXGOlW447J6v2+jtq6Q70ELV/bq/Z36sND4/AFJvfM9vHfcCJLyzuNdyMoPjDT0VF6LGpwoSpQqULJI3n9Zby9lypVEKQlCoMoJv/O7X+Wbf/wv+dyrn6esJjnSP6AKhRApXbqUCkLy8oshEdI2BXwkDTgExoZUFk9KS1I/wr3uEVVanhiE1reSOUNAs7Vpmq9re4xxWDsoThj6Xm/XoyCpx/uQpKAuVysuLpcsVysWiwXaaNquY7la8vDh29tYsuGlQGvNm2+++Z7+hj8JJO9En6bevKMqYhIZVmXO2pzCFwpZIJGURYUUaVuqFJhqvSBISZASVUz52u/8E/7kz7/JZz/zOabzWQr0zdJGwktC78ApJGQZqhSjpaKi9AVSC6RLBCWsRMakFh4hee4NQlhCZnd1T5Tp+RHDWlSeGhymbQc3doZpbxGopEeJ9EIVY14bKwt0lLSrNZfLJVpYECF7gmqs01irsS49O/i0ruqCwFjP8uyMrmtp2jWn8wn98pzVo3POH53z4MEDpBLJsSe/APW95o03bv5zAgdj6uhIvT8aX7Fe6sOLr9R6WKgen9krNjJ0MtEclhn3bHRNLpvWs7hCPvW43+Muja/fI5pRw6PyA+9da00ddPeamkYH673N4fN+4UaQVAjpH99YngjYDtZDPJNUBUoVyYFCJJVyKRWqKJGqIApFEIJqNuXuvaf5lV/9Nf75H/5zfvbnf56imhBFml6xxqRMuEoljbUQtwODFIphImanC5EJTGQTS4zXqOKo5PUYNP/2FCdEIsQhNiuEFBOTyCMNlMY6eq2TC3r2QHQ+KXQb67HW5+tAa4vuNV2nWa3XrDcbuq6nqipW2XrabNZcXl6yWCy2WoDDW/+QDPImI+ScTTEm303vwAdBkCU+wHRWQRQU5RQpChQFUlX4oNILTlEhywovCkwQlLMpTz75LF/8wq/yL//kG/zcz30CQZWtFtC6h1JRVBIXfXpBEZEok5UkUgw2MSaZqcFCSsZzwAVLVIkYhIgQUrRu9DEZV0EkQs1pMBKpJcs7QFrbUkkNImmESBwFIQi8VOggaNapXduv8MLRWI8joHtNbyzaBPreImJBiGCNxfR9sqi7DefLFecXG2anUy4vWzSSddezbHruPnGPZt3lqeikmGGs+ek9AD8ohsEd9kfv7ffO8rl+MK234/be+fqgzB4Z7Ahw69xQH1hPNdvjh2y4JbOh0XpMVrtje4R6UM31RDje2D+/u79R43ub48oez91X6HDo8/uEG0FSMevUeZ9EUndOFEmBQmVySlN9ahd8qxRRJh9fT0Q7h0egfURNF6jpKb/46c/zN//L/853//X3ePmVj7FaN8wWJ0wmMzqtc44ciUJSCkWpCipVUsqCQqqcPyerBIxspyAGl/nh807TfVdpTFyZAhy0A5PFZ11Khd5rQ9v39L1Fa4cxDm08PgqM81jn6TrN5cWS84sll6sVbdslq1GVOJ8U4Zumoet7VFlw+85tNu2GTdNwdn7OummYzGY/vj/w+4RIRKpIcCk7sQ0pODY6y3wqcdpSTgpUVVLOqhSEKzxTlWKepJRYAtZbopS02lNMZxSzU3725z7F//x//G9893vf46OvfJzluuP09m0m5Yy+N3gTKKyi9IoqKEqlKKSiKgsKIVFZpFgSET6tcyols8cfeT5XUGSPVaEEsYwp1kqKJDURQgoCFiKRn4g5A7MkFiXagTUxrZ/1DopIxGKcoXWB5WZD22istqwbjXaCrvMIJH3Xs+4cF6uWi2XDpu9Yt5boYbFYEDwse8dbDy+4PLtEhcjJbErve3rTcnF5xnqzYTKd/pSfgh8ANVfG5fqaIum7fszAPrYI6ivnrrtgcKao8yhd71oY8UW9d93e+s7Q14EsthfWo4F/u7FdNxpIbEe9+03t2h6+6itlxju7Ho5PPub2D3+7u1/a+4YbQVJCDHNnMVsRIcfDpH/gyZLKDg0yiV0OSuVp6srjQkAWyY1YW0vbG8rplMXtU1748HN89Wtf45/9F3/IJz/9WS6Wa3rnELIgu/eBUgipUDJlQU2J5zLB5L7tb4fssv7e7n2ID5OITL4piFkIhbWetu3ZNB3rTcumbdm0PW1vMDopXsuko5M8+5wj+EhRVsznJyxOFknPbaTCfnJyglSKtm0x1rLarOm6jqqq3tuN/AQgpST4RObWp2SDAY+MDjUpUbNFEpvFIwUUSjAtFeVkAuWETvukGlEqXHA4HJtOoyZTbt27zfPPPcNXv/5V/vN/8Ud84hc/xcX5JcF7inKCkiVSJDFb5C7+bvucxECRwwwkApWni52xRCWJ5OSGe843IblARPJaVJriExFwSY8yhIhz6aqqlExFRLpIqcpk9ZcK4yJNa+g6gzGa8+UKMZGsNw0IT99ZiqJC+EiwjuA8AoUsKqaTBbP5HFkIdNuhpGA6K1iczCjKknaT1rnarvnAPCe7Eb8eGQ/16Nz4e/985plr6huPvfWVc/ukwtayOSSOrWfgdQ2NLJnr2t/e0gH71AcdH/+8Uj9XyeewnevO7ltLew0fXPG4un903AiSCtEjRFpcDiGlmwjeEYJLkkkMiQjjaMotrQ7FGPJ6TIoxst7jY8TGiI0Cj6LRnpO7T/Dlf/SP+aNvfJPnnn+RiML6QFFVFFVJUZQIJXHWZSukwGhDqQoKpbInYDhwlshTlTFsieBQqHbv2EjRIgngpoV4kRMpEsWQnQFBWmtzPqC1Zd20NJ3G+UCI0PYa6wLOpSlAqRSyKJBKJU2/2QyZ5aGEUhiX1DiKosA5y3wxSwrxeZHemJs/jRMGSaRCEINP2nPWEkWg7wwxOmQODFAquYt7ItYnLcdyNtkqbwxhD70VaC+JRcGq89z70LO89uWv8I1vf4dnnn8JFySb3kJZIKeKoipRVUH0ITvYCKx1FKJI6TxEJIgABahKUU3LHLCdVC0CCh92Lz+SLHwbA1J4hEqBwEiBVCkRYhSRApA+IEnu9tEFQmtRxJyyI03FtbalbztCXq+8XLWEQkKp0nNCInrnI5PplOl0hjMerQ3ldIJ2Btt7FnfmdH3HfDFDdz3BxRTw/AF4TmA3cNfD/gEp1OOfu8N71s/4aD0qV+8dHY3Vw3f+bI9fIZVxgVEr2TLa9qne70d9sL83PbdfM+PKD/cfj/pgs756iKv937fG3okEfzTcDJIKjkBaEHbe4nxKJ+GcwTlDCIYQLSG6vG4VAE8cPsITYhp4/DB1KCQmSNa9IxaScloxmS/4jS9/hX/7f/47/vE/+Tq37j7Bo8slZ8sV2nusDxjvMNayXK956aWXUmyWEPkXld3WsxpF4pyY34Qf70axJTUGx4mrXn7DtxjiZ0SKlfFBYFygaXuaVtMZi/UB5yLGBjZNx2rVZG9niTGWTdNu16sGfbm267DOcnp6ymQ6oSgK3vj+9+m6DinlB8IFPQSf8tlGj3MGq1uQ0GqLNpqy8PSbJd5YdO/TOopzeG8oSxDS4p1FW48JkSAEXgClYtkEvCooZopquuBXv/wV/u2/+7/4T7/+deYnp1x0ax6sVvTOJVHjzmIazWa94YWXX6GczhHZxdxHh5BJg88Fn6buZHKCkCGiIE8JSogypeQIEYfCCZkSJaqUYyqI7FQTQCKxCKKMiBhQqqI1BcFKYhORPvDo0SX4wPq8TSrqUtFpy6PzSy7bdVqznZR437PpWzSCYnGLEARRwnq5JArHYjrn5NYps5MJb7z9gKbpUyzeB+A52UOdB8067+wOX28djM/X1w/w2zWnd2pzO3LXOwumHven3h/cDyrcv7w+7NCucL0rf0i2B71+XG+vRz00PuxeT9xwcB/v9rv5EXAjSCpF8SeSCj4l8fPBpo+3WGuwNhGW8xrnTXqL9mnQic4QnCN4SwguOxhEXIgUVYGLigdnHS4qiumcW/ee4l/9IeFWEgAAExRJREFU63/DX3zvL7nz1FM8+ewz6Oi42KySWoEULE5PeHR+th28h3WnnVLE4J71zhgnUdx9J4eQfZf03fYQgzXoGIZIUki3DqMdxgaiKACJy8SKUkkMFVLq8Jh04ZBJD7DrOrQ1qDItoDdth4uBajJhcXrCZDp53/6aPy7EbHE7Z9JLCR5je4R0WGNomw2TWwXGOwIG61ImY+ssxvQ43WVvO4uPBpuCy2hbS1FJjBW8+f0OT8HJ7VMWJ3f57l/8V/z1f//fcefOkzz1oWewBB5eXlAspoSpYnFrwYNHDzBGJ+ULmRJTxiARAkqlQApiVClJp0+uEcnDL6XtCASCFMkTURQoJEoKVKGys1B60RAIgpAUk4qyKglIPJI7d05wNuKMxyFZ95beeWyIIAskiigKXEhKF2VRID0E71JYhnE4Keg7nQLGRUTIkrZzvPXmJT445vMJt2+fMJt+EKb79g2Yd7Iw6mvL52P1bnCuD06k8qMaRwV25/LnHcpd6XU9lKt3h/bIaVd23PcdWWz3Ds5f09aBpfTYYo+pp9724qCl+v21pm4GSQ3SQNHjo00k5W3ONGswpsuf/srHmg5reqztsc5grcM5h3Ee4zytDpyvNCiFmkwwXqCqKbfvPsEv/+qv89/89X/L8x95menJKad377JsW4qqQhVlyjeVl8sGDy0h4oEd9C639hhnCsk4yPc6yypN90iZppF8XnfSJjlPhBjxMU39yTzVBBIhFJCSOiJSOgrdJ4FZax1t19G0DW3XslgsODk9YTqdXqOMcQORg9iEiFhvgYA1Gu8cSjlWlytM2+BcygfV9x1adxitCV7TtRucbnCmw9k0Vdj3Fo+gNQETI6KQTBczmsYzPT3h5PZTfP4Lv8pf/vVf8eyHX6SaLrj75F0u25ZyvqCcz7DapJUmmTKJDFJXhJg9+ZLMV4wkfb7BJRO2DhVCSGQE6UHFZFmposTrNH0tSKEaUqapToJHEKgKQVUpxLTAeIGzkhAVTetom54YYtJ8bDQyJtLrO08MEkxABI/3KU7R6I6AT6onRtO0DcZppvMFi8UJk2rCDxK8fjNQ70KL2CesK1v1lVOjQ/XIEmP/aD0qOSKi1N5u/zpHgiuEOJDZtqlc07jcAdklHhzRwV4frrZ52OK2/qHoqFPbzVE1Y9qrR70cV7e9+N2a/yFwI0gqKTok+aG0DrWzopxPAavG5pgPa3Lsx7CvsbbHO721rpxzOG/TP05jkYVClAXapbUqH+FsuUFNZnzm86/yx//ln/HaP/oKizu3mc7nGOfotGY2P9mzdoZ1qGGa752m+HbX7K4df3PNtN/gBhZHskqpuCCEiHWeXmvark9BvdrgbHKM0FrT5bQNSag2qUh0XUen+60jSts2tG1LjDFNZeaYKfeBUBLIGofR40PKjwUOGS26b5nfnnJxtgJncFbnZ8FgrIZgcX1L0B3eekKOzbMh5ZHqO4sPHjEp2eiACQJjHI3uiVR8/ktf4M++92d8+be/wuzWLSaTKW2rWa5a7t67w2xa5my6ydknkZZAFEmBIiW0jWn6LiuaiBiyR+fOM7AgPxVC0bea6XyGcCEFK5dFOhdImn4IApGm6ZnfmeKReGvompYgPJ1OVpLVDoj0EZy3dE7TGk1wGmc7iIFed2jXg/B4rbHdhmgtBYJpOUOo5JRkPwDTfTXXDNTDYF5fLTcyAEbn6hEB1GODCEbn9qb1Rm3W2zbHte5dvq13XH6vc9t2B3ul3ju1a2/YqfePjEntnb5H9zVs75FoPbRUjw7V7DHZtr/XVfDecCNIKgmsJivKeYNxfbKcbIexPc6lAcdYnY91aNPQ6watNxjdYkyXrClrsN6mwFebXLVFUbDuPb2LdDbw92+dM781xyswAp7/yEt860+/w59+918hqpJYKlAqqVgcpPIQQuYA3MCe+sRjMPDYQGjvSGyDyTbEyoSkJOFzJlltPat1y/n5BWdn52zWm6zW4dmsW5arNW3XYkwSFtWdoWkatNZIlQa5IZ3JdDrdxkgJIZh9AFzQlYwUEURepLHO4I2mbRuMdawv15SzEuM0xrTofkPTr5Nm42aJdwbjNb0zWV7JYq2lN4beRhAFrQ901tO7wBuPVkxPZvhCooXk+Zc/wre+/cf8+V98N3kTTgrKxYyHl0u8Ij0TxieJ9GExKUsdDWEMKc4qp/hgIKSkOIFMHn9RRkQJRSWIuOTKTsDnuoQQEFLcmMxheqvzjlgWFIuSTjtWZys2lxc8PHuAXl0gug3SGzaXG5quozWaxkHrHCZPlfbWIbKqS+c0Go2YKArlIabMxNPpzX9O6u2nvubo1cI1152qrzgqbKfwhq+6vmYs3rV+1Qd8W9Guf2OrY1Q8Ha6v6flQKF27JbsrfdiVOTj62O/D7uy6u+3t6Iqh4Gh7dPH7yFE3g6RCcGmKz1mcG5wm8lqUs3kab2dJGaPRukfrDq17TN9hdY8zfSqzlQ3SrJoNb7x1jlQeoQS9jtx78h7nlz3n5y2btuPtR4/4/oO3+fArL/MvvvUNnn/hheRdJfd/PTsHiB9sPWrAdQS1T1bX1+VDElWNIqkXAFjvaPqeddPiUwoutNbJeyx7sQ2K7y6rvKffcVqNmUwnVJOCkNdjggBZFkwX8x/4fn5aMCa5WVtjsa4jOJum7YJLFlHfYXSH1g3WmhQEbWyaDjY97WaD1216TozG6A7jDB5H63r+w9+eUVUwPy1p+8ATT9/j0XnHctWzXHW89fYZr7/5gBdffolvfudbPPf8s2htKYqSvrcgZYrjI1KVKuWiknl6OGtEJp3GtBYlldrazDEkoeVkhSVZ4yiS12qQyU1IkUjJ5heLspSIAMYFZAmySNOVolJsestl03Cx7IizKUZEgk6K+t4EQp4GDi4QQ1Kh8DoQrMBFQTmd4JVDTCI2BrxUqNmUycnip/oM/GCor9nKe/sHdmV2P67WMbIY9iyI60b0vWtrxmQ17tXw37hP9fbcqK2hnXGt9dVrDuvfNV+Pq3gHHF5d73W/Hp+rx/eQt2v2+vsPj6SiIUZNiMN0nc5efgbvNc5prOuxrsOYBqvb9DENVjfp2zYY0+4+ukWbHu8tRaG4uGw5u1ihfc+j8zNa0+FwRBlRlcJGy7Jd8tGPv8Jv/+5v86lXP8l5e4HB4mQgyEFRIGwH/q0n/J76xD7eiaB2x3YBvYiQdNLjkBFLoISgVEkGipDWJow2hJDihdrW4FzEh6Q80el+2xMlS4SQOOvxLlJWE6QoMTrlsLI2ift+ELz7YrRILNGnlxhtOqw3aNPhfI9QAWM7fHRo06a1Kd3ifINuVzjXYu0GazYY3aBNh+kb+raBYCmnFW99/5IHby6xXvPg0UNWXYOJNrmUT0qM8Fz2az7y8Vf46u9/lc/98qd5uD7HyEAfkyJ6UBHnXQqN8DEpSwB4l5TaYyAAdpACCz5P8qb0HiGI7N6eMgJkmdosyZTiqEKwyChQMSJDytarG08RCqqqJAiBdpFOO9ZLg5NTehOIUWJM6pt1GqUUVjtkkIlEvUVYqMo5i+lt2mWHKgr63tL3Zhtkf5Nx+M4/3q7Zx/jY/sA6ENJQX31Q89Va6tFufXh+qOe6NrZMUO+dudLCUCyXH9HYPukNR+rx+XfDYYl698vZ1pO/R6S5PVM/juLfO4r3sa4fGaIwOCzRWShLRKgw1hP9IIMk05um9zlpXIScHHFIN699QVVOmUxmGDvF+Y6ymjCppsQ4Qaqkco7xWNdjrMZZDcJycfGQGBwCz0XzgKc+do+vvPibrOJDXv+Pf8vZo/O06Owk8+oE0xkm1RzjTFazTnMuyTV++E4YJG+AZL2w8+qTORg4DuJ9MdNclkzKy1EQk/u1AApRIhB0reftB5cYGzk9XTA/ndJqQ68tt05mqHJGpy0uBtqmR5vI6cmMvguszIZCSQqpWEwnXFwsubA3XxZJlWmQdNaiZqCYYe0aaSVTNUO3DikkhhQMXkjwQKUEnoAqJqzbJbPJHF/NsW4GIT0nMcwop1NaF2gdSOV3U8xdy2QeeOPNNylLSRCG1eYBT3/sHl954TdZiof8/b//Wy7OLihEQbCwKOe43lBNFxhviVEwrQQ2ZOkkJOn/YT0qkVCMKT1HsBBFcp4RBIKQIJKzhBQp2693BoFgLl2K1SpK9LKnqCQnt06TfJYPPLxcE0XJyYnkuRfu8ujBJUVhOJ3PCF7RRUdE0jhH10fuzWfYBlZnG8qqInq4NVtwfrliZc5+yk/Bu6P+gY7X7MhjGIuvXrk7dHiuhgMKGBetD0qkw/V+fanR0bTh7op61PaWAOrxdWzJ47F9fwyB7ArUh8x8PQ5vZLxTs9efHYE9jtB/eNwMSyrkwN2o8V7jg86xUqOpP5fP+XTO+x7vOrzXWN/hXIfNH+d12rYdxrT0ekPfb+j1ml5v6Po1fb+i0xuaZpnWtlyL9h3aNVw251hp+ewXP8MffecbfOYLn6WYFminU4S/NeheU1UTxBUralir+tEwxEqJrUZS+mzjp7YfSa8tm6ajafrkRJEDfdO0n0sqFMZncofg05u9d8kJI7i0n97m39vf8CeBGALYSLQmLe73PaY3xOjp1i2zWxVN16bnwyU17+B7rG4JwSVr3HR0ukXbHuc12rZo29H1DV27wpgG7zt039L1K7ReoW3Len2Z1kHNBus6tG65uFwSYuBzn/sM3/qzb/PZL34WVUm0M8hpgRcO2/WUVYUsk/OLEJFCRgRJJNZnMVofs1OF2q1bxRxArgREPMb4RFbRI0niuhCI0VGU4IyjnJTJ7b7TVCKJ4hqrWV5c0lxuaNY91bzA+TQNaW3yFO3b9G8NwAZPZ3pEKWk6i/OR6AKEQPAfFO++d0M9GviH8b8en2Z/MB5tj/bfaYyvD3bqx5zYuavvTqZD9dVrMqHl04OvxDWUsKsvlav3ztXXdr6+srfv/l5vr91eWl+5YNvP9ws3gqR2MVF+K+9jzeAokT353M6zz2XvLetMckXXes/7zxiNzutWXd/SNE3+bNg0azabDZtmQ9OsabsGrfvs9bbBWM3l5QWXywueePoJOt3w2mu/zh9/+5s89/xzNH3Dya0F9564R9c176qE/vjpvh/MO3B8TdyaWmlfZ6HQ5TLljTLGZCULj9bJiUQbvZ2iCTFijcXlab6ua9Fab50pbjpc1FgMjpTc0ROw3tHqHuscm80GUcUU6Os0zhqs7rHeYkx6sei9Sw4TxmB0ek6MSc9J27b0fUvbrOn6Dc1mw2azoWnXrNZrXLA0XUuz2WCMYXl5wfnFBU8/8zTNZsmv/8av8e0//RYvvPgs627DfDHjzp1b9N0G51IOKCFlDurOrukivTykvJspb1XytwhIyTYvlVIpPs+7mMMNRH6HEQQ5wdqUpyqtNZJd1gNCyJyHLKWbX11e0q87qkmR/60ZrEkEFX1AyqQWHyN0TU8ksGk7uj79Pn24+dPCj2OHKzwz/Kh3pHBtHQfXjY9daeq6sofX1Lv9kS3EmAR2ZetRvTV7HoO5z8N/496MLbN6qIf9Y1z5WV81kraksztaj++BndW0v1Y2KvAecSNIKr0N5uy80WcXcoPLcVLOWbwbYqc83rntOesMxpq0tpK9tdI/PJMJy2Qni56u7+m6jr5v6fueXvf0fYuxOmftbVBKAZFN07DZbLh1eouu6yDC1772NX7/936fZ599luXykpOTk8dEjVx/dKzwvju2k046/BwyYJoY2pGbD1kKp21puxZrzVb30LuUan0gICllyjflHNGn33HTtHRd94EhqSACXlqscLgQ0N5gosZGh42GtunwzqVnwCeZLBdSOEJvDb1NXn29c/TWYb3GOrvN1dXpjrbrafqepk3PSdf3KX9X36XnpNmwaTfIShKVZ9WvWTVLbt29RW86EPk5+d0/4Kmnn6HpGk5OTrI+pMS5iLaREHNwN8NLzM4TVAypOnx2IiWljqnK/DfMqeeJSSbJOQhRImKavy8TTyWVCg/OeLpec3Z2iXUWYqBZtbiQMkFbG9C9wbss6qxEsqqixzrLpm1p+j5l+P0APCdXBtCag4G13n4dHho2R7t7Vs3huf2G9yuqxwQ5qnjv+q05dNj5ofDQ//pKkdStvZ6+Y7e297HfgS3J7Ld/TR1DPUO7434w3Mr1178XiB/mbf6II4444ogjfpK4IZbUEUccccQRR1zFkaSOOOKII464sTiS1BFHHHHEETcWR5I64ogjjjjixuJIUkccccQRR9xYHEnqiCOOOOKIG4sjSR1xxBFHHHFjcSSpI4444ogjbiyOJHXEEUccccSNxZGkjjjiiCOOuLE4ktQRRxxxxBE3FkeSOuKII4444sbiSFJHHHHEEUfcWBxJ6ogjjjjiiBuLI0kdccQRRxxxY3EkqSOOOOKII24sjiR1xBFHHHHEjcWRpI444ogjjrixOJLUEUccccQRNxZHkjriiCOOOOLG4khSRxxxxBFH3FgcSeqII4444ogbiyNJHXHEEUcccWPx/wNsu5cA++cG1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示原始图片  抵抗样本 以及两张图之间的差异  其中灰色代表没有差异的像素点\n",
    "show_images_diff(orig,adversary.original_label,adv,adversary.adversarial_label)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "advbox",
   "language": "python",
   "name": "advbox"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
